Business methods and systems for offering and obtaining research services

ABSTRACT

Systems and methods that provide for automation-assisted research into the workings of one or more studied systems include software modules that communicate with domain knowledge bases, research professionals, automated laboratories, research service objects, and data analysis processes. When implemented in conjunction with online business methods and systems, ordering processes can interface directly with research services, such as medical research services and biomedical research services. In some implementations, automatically selected research service objects can correspond to 3rd-party research services that can produce research results, and subsequent data-processing can update the knowledge bases and provide guidance to a next phase of research.

CROSS-REFERENCE TO RELATED APPLICATIONS

Pursuant to 35 U.S.C. § 121, § 1.53(b)(1), and § 1.78(a)(1), thisapplication is a divisional application continuing from an unpublishedU.S. patent application Ser. No. 13/351,210, filed Jan. 16, 2012, (andsee U.S. patent Ser. No. 10/311,442 b1, issue date Jun. 4, 2019), saidprior application being a continuation-in-part (C-I-P) applicationclaiming benefit of non-provisional parent application, U.S. patentapplication Ser. No. 12/009,793, filed Jan. 22, 2008 (US Publication No.20080215364-A1; Publication date Sep. 4, 2008; now U.S. Pat. No.8,099,297, issued Jan. 17, 2012), which application claims priority fromU.S. Provisional Application No. 60881638, filed 1/2212007, which parentand provisional applications are, hereby, incorporated by referenceherein in their entirety, and the prior CIP application further claimingbenefit of non-provisional parent application, U.S. Pat. No. 12,290,731,filed Mar. 11, 2008 (US Publication No. 20090138415-A1; Publication dateMay 28, 2009), which application claims priority from U.S. ProvisionalApplication No. 60/985,160, filed Nov. 2, 2007, which parent andprovisional applications are, hereby, incorporated by reference hereinin their entirety. Reference is also made to related U.S. Pat. No.8,571,887 (see U.S. patent application Ser. No. 13/339,370, filed Dec.28, 2011, being a divisional from U.S. patent application Ser. No.12/009,793, above).

FIELD OF THE INVENTION

The invention described herein relates generally to the field of onlinebusiness methods and systems for supporting, inter alia, the offeringof, searching for, selection of and purchase of research servicesapplicable to a system domain under study. Such systems can include,inter alia, biological, environmental, and other energetic systems.Research services can include, inter alia, biomedical.

BACKGROUND

Research into biological systems is moving from manual experimentaltechniques to robotics, and toward automated fluorescent detection inhigh throughput and/or high content screening. Continuing improvementsin automation and data processing are useful and important. Specificadvanced software technologies within the bioinformatics industry,particularly association mining, reverse engineering, knowledge assemblyand simulation components, have enhanced computational biology to createnew capabilities that are needed to improve and accelerate biomedicalresearch.

As shown in FIG. 1, it has been understood for more than fifteen yearsthat likely consequences of global warming will impose damages throughstorms, storm surge, erosion, flooding, disease vectors, sea-level riseand impacts upon domestic water, among other impacts. Uncertainty inmeasurement and modeling, variability in human perception of risk, andavoiding costs of precautionary measures all played together to leavethe city vulnerable.

In U.S. Pat. No. 6,448,983, issued Sep. 10, 2002, incorporated herein byreference in its entirety, Ali et al. disclose a method for assisting auser in selecting an experimental design by obtaining attributesassociated with a many experimental designs. Wang et al. have previouslydisclosed a computer-implemented method of designing a set ofexperiments to be performed with a set of resources (U.S. Pat. No.6,996,550, issued Feb. 7, 2006, incorporated by reference herein in itsentirety). D. R. Dorsett has described a computer-implemented method forprocessing experimental data according to an object model (U.S. Pat. No.7,213,034, issued May 1, 2007, incorporated by reference herein in itsentirety). L. B. Hales et al. have disclosed process controloptimization systems that use adaptive optimization software withgoal-seeking intelligent software objects (U.S. Pat. No. 6,112,126,issued Aug. 29, 2000, incorporated by reference herein in its entirety).Bondarenko has described a system that digitally represents anexperiment design with a definition that provides the logical structurefor data analysis of scans from one or more biological experiments (U.S.Pat. No. 7,269,517, issued Sep. 11, 2007, incorporated by referenceherein in its entirety). Lorenzen et al. disclosed an expert system forthe design and analysis of experiments that includes a descriptivemathematical model of the experiment under consideration yielding teststhat supply information for comparing different designs and choosing thebest possible design, providing a layout for data collection of data,and the system Once the data has been collected and entered, the systemanalyzes and interprets the results. (U.S. Pat. No. 5,253,331, issuedOct. 12, 1993, incorporated by reference herein in its entirety).

U.S. Pat. No. 6,615,157 issued to Tsai on Sep. 2, 2003, hereinincorporated by reference in its entirety, discloses a system and methodand computer program product for automatically assessing experimentresults obtained in a process by analyzing attributes representingexperimental results of a process. A method and system for managing andevaluating life science data is described in U.S. patent applicationSer. No. 10/644,582 (D. N. Chandra, et al., filed Aug. 20, 2003),incorporated herein by reference in its entirety, where life sciencedata is placed in a knowledge base and used for creating a knowledgebase. U.S. patent application Ser. No. 10/992,973 (D. N. Chandra, etal., published Jul. 28, 2005), incorporated herein by reference in itsentirety, includes methods for performing logical simulations within abiological knowledge base, including backward logical simulations. U.S.patent application Ser. No. 10/717,224 (D. N. Chandra et al.), which isincorporated herein by reference in its entirety, discloses a systemthat uses an epistemic engine that accepts biological data from real orthought experiments probing a biological system, and uses these data toproduce a network model of component interactions consistent with thedata and prior knowledge about the system, and thereby ‘deconstructsbiological reality and proposes testable hypotheses/explanations/modelsof the system operation. U.S. Pat. No. 7,415,359 issued Aug. 19, 2008 toHill et al., which is incorporated herein by reference in its entirety,discloses systems and methods for cell simulation and cell-stateprediction, where a cellular network can be simulated by representinginterrelationships with equations solved to simulate a first state ofthe cell, then perturbing the network mathematically to simulate asecond state of the cell which, upon comparison to the first state,identifies components as targets. U.S. patent application Ser. No.11/985,618 by Hill et al. (Filed Nov. 15, 2007; Publ. No. 20080208784,Published Aug. 28, 2008), which is incorporated herein by reference inits entirety, discloses using a probabilistic modeling framework forreverse engineering an ensemble of causal models from data, pertainingto numerous types of systems, and then forward simulating the ensembleof models to analyze and predict the behavior of the network. Hood etal. (U.S. patent application Ser. No. 09/993,312, incorporated herein byreference in its entirety) disclose methods of predicting a behavior ofa biochemical system by comparing data integration maps of the systemunder different conditions, comprising at least two networks, andidentifying correlative changes in value sets between the maps topredict behavior of the system. U.S. Pat. No. 6,493,637 issued to Steegon Dec. 10, 2002, which is incorporated herein by reference in itsentirety, discloses a method and system for detecting coincidences in adata set of objects (See also Evan W. Steeg, Derek A. Robinson, EdWillis: Coincidence Detection: A Fast Method for DiscoveringHigher-Order Correlations in Multidimensional Data. KDD 1998: 112-120;incorporated herein by reference in its entirety).

U.S. Pat. No. 5,384,895 to Rogers et al. (issued Jan. 24, 1995), whichis incorporated herein by reference in its entirety, describes aself-organizing neural network and method for classifying a patternsignature having N-features where the network provides a posterioriconditional class probability that the pattern signature belongs to aselected class from a plurality of classes with which the neural networkwas trained. U.S. patent application Ser. No. 11/668,671 to Shaw, filedJan. 30, 2007 and incorporated herein by reference in its entirety,discloses a computational method of determining a set of proposedpharmacophore features describing interactions between a knownbiological target and ligands showing activity towards the target byidentifying a set of n-dimensional inter-site distance (ISD) vectors.

There is a continuing need to improve the conduct and data processingaspects of research into complex systems. Particularly, there is a needto improve access to automated experimentation in order to acceleratethe pace of productive research. It is a further goal to provide abusiness method and system that employs computerized, automated stepsand standardization at numerous points in the process of providing amedical or biomedical service.

SUMMARY

The invention provides for automated research systems and automatedresearch methods, useful for studying systems, particularly complexsystems. More specifically, the invention generally includes a methodand system for detecting, monitoring, modeling and managing systemicfunction in complex biological and social systems, including, forexample, without limitation, a method and system for finding cures fordiseases in humans. Further, the invention provides a method and systemfor finding cures for diseases, including hardware, software andmaterial inputs, and including automated experimental process connectedto an analysis and modeling component, coupled with a management andquery component, and further including a business method forimplementing the research method and system in the marketplace withbusiness partners and with customers.

The invention provides further for an automated biological researchsystem (ABRS), comprised of multiple hardware and software componentsconnected in such combination and sequence that (i) a connectedseries/set of research steps is automated to accelerate a goal-directed,search-function-based, iterative experimental cycle, (ii) completefunctionality for each of the connected series/set of research steps isincluded, and (iii) these steps provide an iterative, looping-cycle,learning process that seeks the research goal and stops when theresearch goal is met.

An embodiment of the invention provides a system that facilitatesmanagement of a biotechnology and/or biomedical research process,comprising: a research component in communication with the biotechnologyand/or biomedical research process which operates according toconditions of the process, which research component at least one ofmonitors and controls the process using modularized code; astandards-based model employed to modularize control code into testableblocks such that higher order modules are built from tested, approvedmodules; and a rules engine component that processes one or more rulesin association with the modularized code to affect conditions of theprocess in real time. Further the system can have modularized code fordevelopment according to an International Standards for Automation (ISA)S88.01 standard. The invention further provides wherein the researchcomponent includes a process control component that interfaces to theprocess and associated equipment for control thereof according toconditions of the process, wherein the research component includes adata acquisition component that interfaces to the process and associatedequipment for the measurement of data, and wherein the rules engineprocesses a prompt received from the research component in accordancewith the one or more rules. An embodiment can provide for the rulesengine to process the one or more rules to prioritize resourceutilization as requested by the research component.

An embodiment further provides for a method for automating research of astudied system comprising the steps of providing an automated researchsystem having at least one computer software module, a databasecomponent for holding a Library of Possible Experiments (LOPE) thatcontains at least two Experiment Objects (EOs), an Experiment Director(ExpDir) Module, a user interface, a computer, a data processing module,an experimental result analysis module, a database object for holding atleast one first studied-system knowledge model (SSKM₁) (orknowledge-base assembly), a research progress evaluation module (RPEM),a module for (i) comparing results to said first studied systemknowledge model (SSKM₁), (ii) updating SSKM₁ to a second SSKM (SSKM₂),and (iii) comparing SSKM₂ and SSKM₁ to evaluate an increase invalue-of-information (VOI) against a prior research goal; and furtherproviding at least a first studied system, providing at least two EOs,providing a research goal via the user-specified goal (USG), causing theExpDir to evaluate the SSKM₁ against the USG to yield an information gapanalysis result, passing the information gap result to the congruencemodule to analyze the highest probability path to reduce the gap,producing a result out that translates into ‘info needed’, passing the‘info-needed’ descriptor to an Experiment Chooser (ExpChooser), withExpChooser having access to the LOPE, yielding choice of at least oneExperiment Object (EO) passing the chosen EO to Experiment DirectorModule (ExpDir) to direct at least one laboratory to process theexperiment, the lab running the experiment to yield parameter results,passing the results to a data processing engine/module, and passing theprocessed data to the research progress evaluation module (RPEM) and/orModeling module and Congruence Module (CM)), updating the SSKM index n+1and looping again unless the ‘info-needed’ gap is zero and if the gap iszero, then stop.

The invention provides for an automated research system comprising: aprocessor; a memory storing instructions adapted to be executed by theprocessor to receive an ‘experiment directive’ indication to run anexperiment; receive an ‘experiment-run’ command to run the experiment,the command being a permitted experiment; determine whether saidpermitted experiment is proprietary as to subject-matter or procedure orother parameter; and run the experiment defined by the experimentdirective and experiment-run command; if said experiment-run command isproprietary as to method or intellectual property (IP) then adjust as tolegal issues, said experiment being run so that a source of theexperiment directive to run the experiment and a source of theexperiment-run command are anonymous to each other, wherein price ispassively determined, transaction is invisible to other participants,and the project can be executed by a sponsor acting as an agent or as ariskless principal.

In order to remain competitive, many research tool manufacturers seek tocontinuously improve overall equipment and research effectiveness. Tofacilitate these improvements, the invention provides implementingcomputer-based applications to employ such techniques as research-robotequipment monitoring, fault detection and classification, run-to-runcontrol, predictive and preventative maintenance, collection andanalysis of data from research equipment, equipment experimental resultmonitoring, in-line QA/QC monitoring, integrated datareduction/filtering, the reduction or elimination of uncontrolledexperimental results, equipment matching, and other aspects of automatedrobot control.

A further embodiment provides for a storage service that includes one ormore of the following steps and/or processes: a Web-based ordering page;the web-ordering page connected automatically to a secondary, automatedservice-order directive transmission; the order and purchase processconnected to an optionally clinician-supervised step of automaticallypreserving and storing the stem cells; the order and purchase processconnected to a step of delivering the storage vial (or ampule) to asecure storage location maintained by a 3rd-party provider (such as, forexample, a safe-deposit storage box in a bank or other facility); thepurchase process connected to a step of delivering to the purchaser aphysical key to the storage facility and lock-box or a “delivery keycode” that enables the purchaser at any chosen time to initiate physicaldelivery of the stored vial to the purchaser's possession.

A further preferred embodiment of the business method includes the stepsof providing for a transmittal envelope that is especially suited and/ordesigned for a particular storage container and routing to a particularstorage facility, whereby the transmittal envelopes bear sufficientidentifying coding so that the envelopes can be automaticallymanipulated by robotic handling, processed, stored and later retrievedand reshipped based on the exterior coding.

A further embodiment provides for transmitting the container withstorage directions and/or with usage directions. The storage directionsand/or usage directions can be combined with other data incorporatedinto a data component that is affixed to or otherwise permanentlyintegrated with the primary container, secondary container and/orspecial envelope or special mailing container.

A preferred embodiment of the business method provides additionally foroffering and selling the service as a prepaid component of anobstetrical service.

A preferred embodiment of the invention provides for astem-cell-storage, service-business system comprising: a database thatstores information about a plurality of medical service professionals,hospitals, clinics or a combination thereof who deliver at least one ofthe services of delivering babies, attending births, taking samples ofumbilical cord tissue or blood, storing umbilical cord tissue or blood,extracting cord blood stem cells, and storing cord blood stem cells;business system software; an application server that is connected to thedatabase and accessible to customers via an Internet connection and thathosts the business system software; a communication means thatcommunicates information about the professional, hospitals, clinics or acombination thereof to potential customers of the business system,communicates information about one or more customers' choosing ofservices of the professionals, hospitals, clinics or combination thereofto the business system, or both; an input means, whereby a customerinputs information to a relational database relative to the customer;and a planning means that plans stem-cell storage for a customeraccessing the system, wherein said information in said database isstored in a form of a relational database, wherein a customer accessingthe application server through an Internet connection causes thebusiness system software to initiate the planning means based on inputinformation from the customer, wherein the planning means connects orrelates the customer's input information to information about themedical services and retrieves from the relational database informationsufficient to complete and deliver a standard service order.

Another preferred embodiment provides a computer implemented system forselling stem cell storage services comprising: a database containingstorage-method and medical-service-provider information wherein thestorage-method and medical-service-provider information comprisesservice provider data and pre-storage processing and storage methods; anonline purchasing interface where a customer or a reseller accesses thepurchasing interface to acquire at least one storage service; a planninginterface where a customer accesses a presentation of available optionsfor medical services to acquire at least one storage-service product; afront office interface for providing purchase order information andmarketing information and for receiving at least one order from at leastone customer wherein the at least one order is related to at least oneof the plurality of stem cell storage service products; and a processorfor processing orders received from the front office interface; whereinthe database, the purchasing interface, the planning interface, thefront office interface, and the processor are interoperably connected.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates dimensions of an integrated monitoring, modeling andmanagement (IM3) methodology addressing global environmental change thatcan be automated according to an embodiment of the invention.

FIG. 2 illustrates an automated research system (ARS) according to anembodiment of the invention.

FIG. 3 illustrates an automated research system (ARS) according to anembodiment of the invention.

FIGS. 4A and 4B illustrate aspects of an automated research methodaccording to an embodiment of the invention.

FIGS. 5A-5D illustrate Type-1 experiment outcomes according to theinvention.

FIGS. 6A-6D illustrate additional Type-1 experiment outcomes accordingto the invention.

FIGS. 7A-7D illustrate further Type-1 experiment outcomes according tothe invention.

FIGS. 8A-8E illustrate additional details of knowledge-base-assemblyfunctions in an automated research system according to an embodiment ofthe invention.

FIGS. 9A-9H illustrate further Type-1 experiment outcomes according tothe invention.

FIGS. 10A-10F illustrates additional Type-1 experiment outcomesaccording to the invention.

FIG. 11 illustrates aspects of a business method for automated researchsystem services according to an embodiment of the invention.

FIG. 12 illustrates aspects of a general integrated monitoring, modelingand management (IM3) methodology that can be automated according to anembodiment of the invention.

FIG. 13 illustrates dimensions of an automated, integrated monitoring,modeling and management (AIM3) methodology addressing water-resourcemanagement, according to an embodiment of the invention.

FIG. 14 illustrates dimensions of an automated integrated monitoring,modeling and management (AIM3) methodology addressing use of globalenergy resources, according to an embodiment of the invention.

FIG. 15 illustrates construction of a knowledge-base-assembly causalnetwork for energy resource systems in an automated research systemaccording to an embodiment of the invention.

FIG. 16 illustrates aspects of an automated research methodology appliedto modeling and analysis of global energy resources according to anembodiment of the invention.

FIG. 17 illustrates functional partitions of the method of building adomain Knowledge-Base-Assembly according to an embodiment.

FIG. 18 illustrates knowledge-base-assembly functions in an automatedresearch system according to an embodiment of the invention.

FIG. 19 illustrates aspects of a business method for automated researchsystem services according to an embodiment of the invention. FIG. 21

FIG. 20 illustrates aspects of a business method for selling andpurchasing automated research system services according to an embodimentof the invention.

FIG. 21 illustrates aspects of a business method for automated researchsystem services according to an embodiment of the invention, being asuccession of web pages or web screens that can appear according to anembodiment as part of the providing of an offer to a potential customerand the recording of order information and completion of the orderingtransaction.

FIG. 22 illustrates aspects of a business method for multi-partycollaboration using automated research system services according to anembodiment of the invention.

FIG. 23 illustrates automated research control steps according to anembodiment of the invention.

FIG. 24 illustrates automated device control functions according to anembodiment of the invention.

FIG. 25 illustrates research modeling components for an automated,integrated, monitoring, modeling and management (AIM3) energy resourceslearning model according to a preferred embodiment.

FIG. 26 is a block diagram illustrating computing hardware and networkaccording to embodiments of the invention.

FIG. 27 illustrates a stem-cell storage container according to anembodiment of the invention;

FIG. 28A illustrates a glass flame-off pipette containing stem cellsaccording to an embodiment;

FIG. 28B illustrates a flame-off step to create a sealed, glass ampulehaving a specialized gaseous environment within;

FIG. 29 illustrates a two-layer storage container according to anembodiment, wherein an airtight, permanently sealed ampule is locatedwithin an outer storage cylinder,

FIG. 30 illustrates a storage ampule upon which is permanently affixed adata component, according to an embodiment;

FIG. 31 illustrates a sequence of business steps according to anembodiment of the invention; and

FIG. 32 illustrates a specialized transmittal envelope according to anembodiment of the invention.

DETAILED DESCRIPTION

This specification explicitly references U.S. patent application Ser.No. 12/009,793, filed Jan. 22, 2008, having priority to U.S. ProvisionalApplication No. 60/881,638, filed Jan. 22, 2007; U.S. patent applicationSer. No. 12/290,731, filed Nov. 3, 2008, having priority to U.S.Provisional Application No. 60/985,160, filed Nov. 2, 2007; U.S.Divisional patent application Ser. No. 13/339,370, filed Dec. 28, 2011;and U.S. C-I-P patent application Ser. No. 13/351,210, filed Jan. 16,2012, all of which foregoing referenced applications are inventions orco-inventions of this same inventor and/or are applications assigned toa common person or inventorship entity as this instant application, andall of which foregoing referenced patent applications are incorporatedherein by reference in their entirety.

One embodiment of the invention provides a method for attaching alearning process to a linked object database (ODB), with artificialintelligence (AI) rules, constraint-based decision modeling, andsimulation based on learning-revised instruction sets, model congruencetesting, model conflict detection, and model variation, in order toconfigure optimized search for experiment objects (EOs) that can beexecuted with minimum supervision (e.g., automatically by robots) inorder to create a desired experimental outcome.

The invention further provides for creating a bridge between currentcomputing and biomedical research technologies and a new era of R&Doptimization technologies based on the most advanced Internet andsoftware technologies by connecting distributed libraries of EOs withdistributed providers of robotic lab services and flexible data analysisengines (DAE) and systems modeling, such as, e.g., cellular systemsbiology research methods, that can be provided as contract researchservices to produce new knowledge.

The invention disclosed and claimed herein, in one aspect thereof,comprises a system that facilitates management of an automated researchprocess. An Experimental Object (EO) research component in communicationwith one or more laboratory processes operates according to processconditions to output an experimental result, which EO research componentat least one of monitors and controls the process using modularizedcode. A rules engine component in an Experimental Director (ExpDir)module processes one or more rules in association with the modularizedcode to control the laboratory process conditions in real time bybalancing process efficiency criteria to arrive at an optimal result.

ARS and System Functions

The invention can include an automated research system (ARS) toestablish a normal set of functional operations in a system under study(hereinafter the ‘studied system’, or SS. In general, then, an ARS willbe used in specific domains of an SS. The invention can include ageneralized ARS that can be directed toward many differing domains ofSS, or it can include specialized ARSs that are tailored for a specificdomain of a specific SS (such as human biology, SS-HB, or globalenvironmental change, SS-GEC). In general, an ARS can be directed tosolve the following problems:

A. Whole System Functions

-   -   Subsystems/module/object/component

B. Problems(s):

-   -   (1) To solve for causes of system dysfunction.    -   (2) To solve for solutions to correct system function        -   (a) single-function solutions        -   (b) multiple-function solutions

Prior observations (data) may have established a normal set offunctional operations (NSFO), which can be described in a manual ofoperations (such as, for example, in the case of human health, one ormore manuals of medicine, the Merck Medical Manual, a standard medicaldictionary, and/or one or more knowledge bases or knowledge assembliesthat are products of companies such as Genstruct Inc. (Cambridge,Mass.), and/or other assemblages of biomedical knowledge).

The ARS of one embodiment of the invention can establish a manual ofnormal operation for an SS by multiple testing of numerous examplesystems, in each test monitoring or observing one or more functionalobservables (or parameters, or factors). The ARS of one embodiment ofthe invention can test dysfunctional systems or functions (or componentsubsystems of such systems) in order to solve for causes of system orsubsystem dysfunction. Further, the ARS of additional embodiments of theinvention can test dysfunctional systems (or component subsystems ofsuch systems) in order to solve for functional solutions (which caninclude added or corrected components) in order to correct system (orsubsystem) function.

One embodiment of the invention provides for a learning machine andmethod of use thereof for learning about any system of any domain,wherein the learning machine (LM) comprises a knowledge base (KB),Library of Possible Experiments (LOPE), etc., and wherein the method ofuse includes providing a user-specified goal (USG). The LM according toan embodiment of the invention includes at least a LOPE, at least twoEOs, at least an experiment director (ExpDir) module and a data analysisengine (DAE).

Experiment Chamber

An ARS according to an embodiment of the invention can focus oninstances, samples, parameters, factors or other measurable aspects orcharacteristics of a SS, where the SS is studied in an experimentchamber (EC, or ExpCh), which EC can be a laboratory, or a series oflaboratories, or a combinations of chambers within one laboratory ordistributed between multiple laboratories or locations. In the case ofan environmental system, such as the global environment, the experimentcan comprise a series of observations of aspects of the globalenvironment itself, either from remotely sensed satellite perspectives,or from measurements taken within the system itself (such as, forexample, air samples or water samples that are taken and measured in alaboratory, or in situ measurements in a body of water, or in theatmosphere, or in a biosphere or ecological location. Therefore, it isan aspect of the ARS to have at least one experimental chamber (EC)where observations are made at one or more time points and/or timeintervals, with the understanding that the EC can be without walls. Byway of example and without limitation, the EC can be a Petri dish, avolume of a fluid between two microscope slides, a cell, multiple cellswithin one or more wells of a microplate, a gene-expression chip, anorgan, an organism, a bioreactor, a test tube, a population oforganisms, a vat, an oven, a target, a crop field, a nuclear reactor, aparticle-accelerator chamber, a planet, a reaction chamber, a virtualsimulation environment, and/or any other volume, region, locale,substrate, environment or background within, upon, through, from and/oragainst which can be taken a measurement of a parameter, factor,function, behavior and/or aspect of a studied system (SS). This testingand/or observing in the EC can include spatial measurements in x, y andz and in time (t), including measurement and/or description of what,when, where, why and how a progression of observed events occurred. Agroup of people can comprise an EC, as can a town or a city, or acorporation, or a subs-population of consumers, or a defined market. Aspreviously mentioned, the EC can be a combination of constituent ECs,such that, for example, an experiment could be conducted in an EC thatcould be established through and over a set of laboratories in multiplegeographical locations.

Note that the experiment chamber can be a virtual environment thatexists in a computing environment in one, two, three or many dimensions.For example, an experiment chamber could include the 2-dimensional andhigher-dimensional test spaces used for studying cellular automata, suchas described by Wolfram (2002, The New Science, Wolfram Press), which isherein incorporated by reference in its entirety).

To the accomplishment of the foregoing and related ends, certainillustrative aspects of the invention are described herein in connectionwith the following description and the annexed drawings. These aspectsare indicative, however, of but a few of the various ways in which theprinciples of the invention can be employed and the subject invention isintended to include all such aspects and their equivalents. Otheradvantages and novel features of the invention will become apparent fromthe following detailed description of the invention when considered inconjunction with the drawings.

Definitions

As used in this application, the terms “component” and “system”, whenused in the context of an automated research system (ARS), which can beprovided by embodiments of the invention, are intended to refer to acomputer-related entity, either hardware, a combination of hardware andsoftware, software, or software in execution. For example, a componentcan be, but is not limited to being, a process running on a processor, aprocessor, a software module, a software object (including an experimentobject), an executable, a thread of execution, a software program,and/or a computer. By way of illustration, both an application runningon a server and the server can be a component. An information managementsystem (IMS) can be located on a server, or distributed across multipleservers. One or more components can reside within a process and/orthread of execution, and a component can be localized on one computerand/or distributed between two or more computers. Software programmodules can include routines, programs, components, data structures,etc., that perform particular tasks or implement particular abstractdata types. Computer system configurations can include personalcomputers, hand-held computing devices, microprocessor-based orprogrammable consumer electronics, and the like, each of which can beoperatively coupled to one or more associated devices. An ARS module caninclude a 3-D, geodynamic, environmental modeling system.

It will be appreciated also that “system”, when used in the context of astudied system (SS) that can be the object of research of embodiments ofthe invention, can be intended to refer to any system of any domain,including without limitation complex systems, energetic systems, dynamicsystems, real-world systems, natural systems, environmental systems,climate systems, atmospheric systems, biospheric systems, oceanicsystems, river systems, biogeochemical system, bioenergetic systems,biological systems, cellular systems, human and non-human systems,social systems, energy resource systems and global energy systems, interalia.

A system can be a combination of multiple subsystems at varying levelsof organization of varying spatial dimension and varying degrees ofoverlap (or non-overlap) between subsystems. Thus, in one embodiment,for example, a biological system can be a human organism comprised ofsubsystems such as skeleton and organs, wherein each of these subsystemsare further comprised of cells of many different types.

A subsystem can be defined as a component, an object, and/or a module,wherein subsystem, object, module and component can be equivalent (forexample, subsystem=module=object=component) and wherein any one of asubsystem, module, object and/or component can be formed, defined and/orconstructed as a set of functions or as a set of one or more tangibleobjects inter-related by a set of functions. Thus, herein a subsystemcan be purely a subset of systemic functions without tangible objects ofit can be a subset of systemic functions in combination with a subset oftangible object components.

As used herein, the terms “infer” or “inference” refer generally to theprocess of reasoning about or inferring states of the system,environment, and/or user from a set of observations as captured viaevents and/or data. Inference can be employed to identify a specificcontext or action, or can generate a probability distribution overstates, for example. The inference can be probabilistic—that is, thecomputation of a probability distribution over states of interest basedon a consideration of data and events. Inference can also refer totechniques employed for composing higher-level events from a set ofevents and/or data. Such inference results in the construction of newevents or actions from a set of observed events and/or stored eventdata, whether or not the events are correlated in close temporalproximity, and whether the events and data come from one or severalevent and data sources.

While certain ways of displaying information to users are shown anddescribed with respect to certain figures, those skilled in the relevantart will recognize that various other alternatives can be employed. Theterms “screen,” “web page,” and “page” are generally usedinterchangeably herein. The pages or screens are stored and/ortransmitted as display descriptions, as graphical user interfaces, or byother methods of depicting information on a screen (whether personalcomputer, PDA, mobile telephone, or other suitable device, for example)where the layout and information or content to be displayed on the pageis stored in memory, database, or another storage facility.

Acronyms and Abbreviations

ABRS—Automated biological research system

AIM3—Automated Integrated Monitoring, Modeling and Management

AIQME—Artificial Intelligence and Query Management Engine

AJAX—Asynchronous Javascript And XML

ARS—Automated Research System

BAC—biomodel assembly component

BIND—Biomolecular Interaction Network Database (http://bind.ca)

BIRN—Biomedical Informatics Research Network

CEO—chosen experiment object

CM—Congruence Module

CompRep—completion report

CORBA—Common Object Request Broker Architecture

CRO—contract research organization

DAE—Data Analysis Engine

DAML—DARPA Agent Markup Language

DB—database

DIP—Database of Interacting Proteins (http://dip.doe-nbi.ucla.edu)

DKB—Domain Knowledge Base

DOM—Document Object Model

DPI—data-processing instruction

EC—Experiment Chamber

ECC—Experiment Control Component

ED—Experiment Director (module)

EDM—Experiment Director Module

EDS—Experimental Design Sequencer

EM—Equipment Module

EO—Experiment Object

EO-Chosen—Experiment object chosen

ESS—Energetic System Simulator

ETF—Energy-Technology Feedback

Exp-CH—Experiment Chamber

Exp-CTRL—Experiment Controller

ExpDir—Experiment Director (module)

FTO—freedom-to-operate

GUI—graphical user interface

HPRD—Human Protein Reference DB (http://hprd.org)

HTP—High-Throughput

HUPO-PSI MI—Human Proteome Org., Prot. Stds Init., Molec. Interact.

IBIS—Integrated Bayesian Inference System

IG—Information Gap

IKF—information-knowledge feedback

IM3—Integrated Monitoring, Modeling and Management

IMS—information management system

INEM—information needed evaluation module

IntAct—IntAct Protein Interaction DB (Eur. Bioinf. Inst.)

IP—intellectual property

ISA—International Standards for Automation

ISD—Inter-Site Distance

KB—Knowledge Base

KBAM—Knowledge Base Assembly Module

KBAC—Knowledge Base Assembly Component

KB-MSM—Knowledge Base for Molecular Systems Model

KL—Knowledge Library

LAN—Local Area Network

LOPE—Library of Possible Experiments

LSID—Life Science Identifier

MAGE—MicroArray and Gene Expression

MIAME—Minimum Information About Micro-array Experiment

MIAPE—Minimum Information About Proteomics Experiment

MINT—Molecular INTeraction database (http://mint.bio.unirona2.it/nirt)

MIPS—Munich Info. Ctr Protein Sequences (http://mips.gsf.de)

MSMs—Molecular Systems Models

NED—Next Experiment Design

NSFO—normal set of functional operations

ODB—object database

OIL—Ontology Interchange Language

OOP—object-oriented programming

OSITA—one skilled in the art

OTS—off-the-shelf

OWL—Web Ontology Language

PC—personal computer or parameters codes

PDO—processed data output

PHP—PHP: Hypertext Preprocessor

QA/QC—Quality Assurance/Quality Control

QM—Query Manager

QSAR—quantitative structure-activity relationship

RDBMS—Releational Database Management System

RDF—Resource Description Framework

REAC—Reverse Engineering Assembly Component

REAL—reverse-engineering algorithm linear

RE-MSM—Reverse Engineering-Molecular Systems Model

ROI—Return on Investment

SBML—Systems Biology Markup Language

SIS—Starting Instruction Set

SLAM—Sub-Linear Association Mining

SM—Systems Model

SOAP—Simple Object Access Protocol

SOMs—Self-Organizing Maps

SQL—Structured Query Language

SS—Studied System

SSC—Starting Set Controller

SS-GEC—Studied System-Global Environmental Change

SS-HB—Studied System-Human Biology

SSKM—Studied System Knowledge Model

SSL—Secure Sockets Layer

SSP—System Service Provider

SVG—Scalable Vector Graphics

SVM—Support Vector Machine

UI—user interface

UML—Uniform Modeling Language

UQI—User Query interface

USG—User-Specified Goal

USG—PC-user specified goal parameter codes

USP—United States Patent

VOI—value-of-information

VOIA—value-of-information analysis

WSDL—Web Services Description Language

XCEDE—XML-based Clinical Experiment Data Exchange schema

XML-extensible markup language

The present invention is now further described with reference to thedrawings, wherein like reference numerals are used to refer to likeelements throughout.

FIG. 2 illustrates a system 200 that employs a rules-based ExperimentDirector (ExpDir) engine 204 for automated research in a studied system(which can be a biomedical environment), in accordance with the subjectinvention. The system 200 can include an Experiment Object (EO) process(not shown) that is being conducted using process equipment 201 (such ashigh content screening platforms, incubators, aligners, and robot armsto move samples from one station to another, for example). In order tomanage the EO process, the system 200 further includes a researchExperiment Controller component (here this component is part of theExpDir module) that interfaces to the process and equipment 201 formonitor and control thereof. The Experiment Director research component204 includes a process control component that includes software and/orhardware for controlling the automated lab equipment 201 that runs theEO experiment process. For example, one module of the process controlcomponent 204 can be a particular model of a hardware device (e.g.,rackmount or standalone) that includes processing capability, memory,firmware, and interface hardware/software that facilitates interfacingto the equipment 201 for control thereof. It will be appreciated thatthe research control component 204 and process control component (notshown) can be distributed in different locations.

The research component 204 can also includes a data acquisitioncomponent that can include sensors and hardware/software suitable forinstrumenting the process and equipment 201 to take measurements ofresearch subjects, samples or the like before, during, and afterperforming the EO process. The research component 204 can also include astandards-based code component (e.g., S88) that allows for developmentand implementation of modularized code for management of the process andassociated equipment 201, the process control component, and dataacquisition component.

The process control component and data acquisition component bothinterface to the process and equipment 201 across a communicationsnetwork, which can be any conventional wired and/or wireless network,including the Internet. It is to be appreciated by one skilled in theart that the network can also be a combination of networks such thatcommunications between the process, equipment 201, process controlcomponent and data acquisition component can be via a high speed localbus suitably dedicated for data acquisition and control environmentswhen required, whereas the remaining part of the network is anwired/wireless Ethernet network, the Internet, or the like.

Still referring to FIG. 2, the system 200 can also include a rulesengine that processes rules in support of controlling and/or makingmeasurements associated with the EO process and/or research labequipment 201. The rules are processed in accordance with thestandards-based code of the code component. The rules engine and codecomponent can communicate across the network, and with the otherentities including, but not limited to the process control component,data acquisition component, process, and process equipment 201. Thesystem 200 according to an embodiment can also include a user interface205 and Query Manager 206 and database server 203. The server 203 cancontain a knowledge-base relevant to the research. A data analysisengine 202 can take results from laboratory equipment 201. Aknowledge-base assembly module (KBAM) 207 can here include a Modelingsubmodule, a Congruence testing submodule and a Simulation module. TheKBAM 207 can interface with the Query Manager 206 and with the ExpDir204 when evaluating continuation of the experimental process intoanother experimental round.

It is to be appreciated that by way of example, and not by limitation,these are only a few of the entities that can be employed in the system200. For example, there can be a multiplicity of processes andassociated equipment, control, and data acquisition components in thesystem 200 each or a combination of which are controlled or controlrelated processes. Moreover, the system 200 can be accessed remotely viathe Internet or a LAN (Local Area Network), WAN (Wireless Area Network)or the like, by employing secured login procedures to authorized users.Such login can provide read-only access, or even provide full accesssuch that any of the system entities can be manipulated before, during,and/or after performing the process.

Virtual Automated Laboratory

Automated Lab locations can be distributed in different geographiclocations and connected by the Internet or other computer network, e.g.,located in different “rooms”, where each “room” could be a differentcompany connected through a network, or where each “room” could be anactual laboratory room in a different company connected through theInternet.

An information management system (IMS) can be located on a server, ordistributed across multiple servers, where each server with IMScomponents has multiple functionality, including multi-media fileservices and processing, flash memory storage, hard disk digital memorystorage, operating software, software to manage robotics, software tomanage network connectivity with other similar servers, software tomanage interactions with the system rule engine (or rule engines) and/orthe system query engine (or query engines), and, inter alia, software tomanage interaction with an RDMS and data mining engine (or module).These servers can be small and portable, being built on technologysimilar to that manufactured by Omnilala, Inc.; (Newton, Mass.), such asdevices employing the VIA Mini-ITX motherboard (computer system circuitboard having multiple standard hardware connectivities and onboardprocessing power). As well, these servers can access multiple systemdatabases, with system ontologies and XML parsers. The system softwarecan include artificial intelligence modules, including inference engines(which can incorporate rules engines that are based on Bayesianprobability methods).

Aspects of laboratory automation (including robotics) managed by theautomated research system according to an embodiment can include,without limitation:

-   -   Liquid Handling    -   Automated Assay    -   Microfluidic Workstations    -   Microplate Detectors    -   Detectors    -   Bar Code Readers    -   Incubators    -   Storage    -   Consumables management devices    -   Robotic Management devices    -   Robotic Transport devices    -   Laboratory Automation Workstations    -   ADME-Tox Workstation    -   Assay Workstations    -   Chemistry management devices

Example 1

Referring to FIG. 3, after selection of an EO and execution by theExpDir module a Knowledge Library (KL) element (which can be an elementof a knowledge base (KB)) and starting instruction set (SIS), which canhave elements of a User Specified Goal (USG) starting instruction, areused by a Starting Set Controller (SSC) and Experimental DesignSequencer (EDS) to initiate a first Experiment Sequence (#1). Results ofthe first Experiment #1, after (i) passing into and through the dataselection/filtering and data analysis modules of a Data Analysis Engine(DAE), and (ii) passing through the Biomodel Assembly and Simulationsteps, are (iii) used together with the KL in the Congruence Modulewhere (iv) an information gap is derived and passed to an AutomatedExperimental Designer Module (EDM) (that can be the Experiment Choosermodule with a random creative design component and/or a decision ruledesign component (e.g., which builds a new EO from closely associatedtechniques of previous unsuccessful EOs, based on expected outcomes ofmany sub-EO technique steps)), in order to (v) produce a design for anautomated Experiment #2, where said design is passed to the ExpDir/EDSto initiate Experiment #2 in the automated laboratory. Then, the resultsof Experiment #1 and Experiment #2 are processed through Data Analysissteps (i.e., the combined results of all experiments from the currentand previous cycles) are combined in the Biomodel Assembly steps, newSimulations are run, from which simulation results and the KL are drawntogether in the Congruence Module where an information gap is derivedand passed as the inputs again to the EDM, which EDM produces a designfor automated Experiment #3, and so forth through as many cycles as areneeded to meet the goal functions of the Starting Instruction Set. Thissystem is graphically depicted in FIG. 3 wherein a robot-drivenlaboratory 305 has computer and software control components, includingOTS Robot-Driver Components and OTS Experiment-Control Components;

a data processing and filtering module 306 (such as, for example, HTPimage analysis and data selection), is comprised of a module wrapperthat controls, directs and operates multiple OTS software components;

a Data Analysis Engine (DAE) module 307 is comprised of a module wrapperthat in turn controls, directs and operates OTS software componentsselected from the set of GLP, Spotfire, SAS, Mathworks, and othercomparable data-analysis applications, and which operations includehierarchical clustering, association mining, pathway analysis, etc;

a Modeling Module 308 can be or can include a bio-model assemblycomponent (BAC) that can create from prior outputs of the DAE 307 plusthe KL or KB a set of nested, hierarchical, node-arc (orobject-interaction) causal network models (such as, for example,molecular system models (MSMs) and/or dynamic systems models (such as,for example, dynamic molecular models), which models allow dynamicsimulation operations to be applied, wherein said causal modeling module(or BAC) includes a Reverse Engineering Assembly Component (REAC) ormodule that operates on the outputs of the DAE 307 to form areverse-engineered molecular systems model (RE-MSM), and where said BACadditionally includes a Knowledge Base (or KL) Assembly Component (KBAC)that operates on inputs from a prior KB to form a knowledge-basemolecular systems model (KB-MSM), and where the BAC further can includea Congruence Testing module (or component) that interacts iterativelywith both the REAC and KBAC to derive a closest-fit resultant MSM byiteratively comparing the first-generation RE-MSM and KB-MSM fordifferences in structure (topology, objects, relationships anddynamics), and then adjusting and constraining a second-generationRE-MSM and KB-MSM by using high-probability information (above someuncertainty threshold) from each first generation KB-MSM and RE-MSM,respectively, to constrain the creation of the 2nd-generation RE-MSM andKB-MSM, respectively;

an n-dimensional Energetic (or Dynamic) Systems Simulator (ESS) 309, iscapable of instancing systems simulators for any system (such as, forexample, for virtual, biological, social or energetic systems) and atmultiple levels of biological organization (including a dynamicBiomolecular System Simulator), and can “run” the resultant systemsmodel (SM) (such as, for example, an MSM) passed from step and component308, where the model “runs” or iterations (a) test the capability of theMSM to predict current experimental results that were not used to buildthe SM (or MSM), (b) predict signaling cascades and events that maymanifest in significant perturbations of certain system objects (such asbiological objects) in the SM (or MSM) (such as, e.g., biomarkers), (c)test effects of manipulations of the SM (or MSM) to simulate a systemdysfunctional state (such as a disease state in a bio-system), (d) testeffects of corrective interventions applied to the SM (or MSM) indysfunctional (diseased) or healthy mode to predict impacts and resultsof such interventions, and (e) test the robustness of the resultant SM(or MSM) by variation of parameters and/or Monte Carlo approaches toyield stability, robustness and/or fitness metrics as functions ofuncertainties in the SM (or MSM) (e.g., such as by analyzing topology,structure, objects, and/or relationships);

an ExpDir Module 310 (which can include an Experiment-Design Module) cancreate, access or derive a set of potential experiments constrained byinformation derived from the USG (or SIS), the KB (or KL), and previousSM (or MSM) analyses, whereby EOs from the LOPE (which can be TemplateExperiments) are modified by random or guided permutation to create aPotential Experiment Set, and where each Potential Experiment isvirtually explored in an experiment-simulation step to produce SimulatedResults for each of the Potential Experiment Sets, whereby newinformation to be probably learned about certain variable objects andinteractions can be categorized and distinguished from controlledobjects and interactions, and where a value-of-information analysis(VOIA) operation (which analysis establishes value in relation to (i)reducing uncertainty about certain objects and interactions in the MSMfrom ExpDir 310, (ii) increasing the robustness of MSM simulationresults and predictions, and (iii) generating additional, well-defined,testable hypotheses) is applied to those categorized and distinguishedobjects and interactions, whereby a next experimental sequence can bechosen based on a function that maximizes the expected VOI from theanticipated experiment; whereupon the ExpDir (or EDM) outputs a next EO(or Next Experimental Design (NED)), which is passed to the ExpDircontroller (or EDS) 304; a Starting Instruction Set Controller Module304 can be coupled with an ExpDir 310 (which can have an ExperimentalDesign Sequencer element). For the first experiment of a series ofiterative, learning cycles, the ED 310 establishes a first ExperimentObject (EO) from the SISC Module 304 (via a USG and the Query Managermodule reaching an EO from a LOPE source). For successive experiments inthe iterative, learning process, the EDS uses the NED passed from ExpDir310 as the experiment design to be next sequenced to theExperiment-Control Components in step and laboratory 305;

an Artificial Intelligence and Query Management Engine (AIQME) 303contains a set of rules, constraints, supervision modules, result-goals,optimization procedures, and fault/error handling supervisioncomponents.

a Visualization Engine 301 can be wholly or in part OTS components,(such as OmniViz, etc.); a Graphical User Interface 302 allowsinteraction with many of the other components, particularly the AIQME303, laboratory 305, object database 312, systems simulator 309 andExpDir module 310 [as needed for human learning and monitoring of systemoperation, and for supervised learning cycles; and a database function311 contains an Object Database (ODB) 312, such as Oracle®, MS-Access®or another OTS application has program connectivity to all other modulesand components, acquiring and storing system information, and holds theKnowledge Base Library (KB/KL), as well as providing storage for theBiomolecular Models and other data and program objects, and contains anAlgorithm Library and Subcomponent (subroutine/object-class) Library313, which is embedded within and/or directly coupled to the ODB 312 andhas program connectivity to all other modules and components. Thealgorithm and subcomponent library 313, may be stored as program objectswithin the ODB 312.

As shown by the connecting arrows in FIG. 3, the database component 311is connected (can exchange information with) the SIS Controller 304,laboratory 305, data processing and filtering module 306, DAE 307, MM308, ESS 309, and ExpDir 310. Visualization Engine 310 and GraphicalUser Interface 302 can exchange information. Graphical User Interface302 and AIQME 303 can exchange information. Information can pass fromAIQME 303 to SIS Controller 304, from SIS Controller 304 to laboratory305, from laboratory 305 to data processing and filtering module 306,from data processing and filtering module 306 to DAE 307, from DAE 307to MM 308, from MM 308 to ESS 309, from ESS 309 to ExpDir 310, and fromExpDir 310 to SIS Controller 304.

FIG. 4A illustrates a method according to at least one embodimentaccording to the Invention, wherein at step 401 a user chooses atop-level domain from the User Query Interface (UQI) and Goal Library(GL) and develops a user-specified goal (USG), which goal can include,for example, such tasks as ‘characterize normal’; ‘detect/characterizeabnormal’; ‘test/find corrective (or adaptive/protective)’; ‘optimizecorrective (or adaptive/protective)’. At step 402 the user chooses USGparameter codes (USG-PCs) in interaction with the Query Manager (QM) forinput to the Experiment Director (ED). At step 403, which is optional,the ARS optionally tests the list of user-specified parameters forcompleteness against a completeness rule and index associated with theQuery Manager (QM), Experiment Director (ED) and the LOPE. If the listis incomplete, the program passes control back to the UQI, prompting theuser to correct the goal specification. If step 403 is completed oroptionally bypassed, then at step 404 the ARS passes the user specifiedgoal (USG) to an Experiment Director Module (ED). At step 405 the EDaccesses the LOPE to extract a subset of EOs that correspond to theUSG-PCs and the EOs can contain, without limitation, data related tostandard descriptors, ontologies (such as ontologies developed by theInteroperable Informatics Infrastructure Consortium (I3C)),input/output, parameters, cost, time and interoperability certification.At step 406 the Experiment Chooser Module begins processing the USG-PCsand the subset of EOs in order to select a chosen EO (EO-chosen). Atstep 407 the Exp Chooser module accesses, or runs, the Experiment UsageEngine (EUE) as part of the selection evaluation, where the EUE can useparameters to search the LOPE and can evaluate a subset of the LOPEbased on VOI and other selection criteria (from the parameters and/orbuilt into each EO), and with step 408 including the EUE accessing usagedata stored in the EOs, and processing this EO usage data together withthe USG-PCs, following decision-rule sequences in a Decision Module(Rule Engine) component of the Experiment Chooser Module.

Still referring to FIG. 4A, the ARS chooses an EO at step 409 and atstep 410 the ED module passes the choice and the EO data to theExperiment Controller (Exp-CTRL). At step 411, the Exp-CTRL moduleaccesses data about available Experiment Chamber (Exp-CH) resources thatcan be in-house and/or available through a distributed network,including at step 412 using LOPE protocols and/or protocols within theEO to instruct initiation of the EO-Chosen at some Exp-CH, such as, forexample, at a laboratory of a Contract Research Organization (CRO) underan automation contract to the ARS. At step 413 the Exp-CTRL modulecontrols progress of the EO-chosen. It will be understood that step 413,differing embodiments of the invention, can include control of anexperiment that is completely automated through a robotic laboratory, orpartially automated through a laboratory with combined work of humanscientists and robotic research platforms, or control of an experimentthat is carried out by one or more human technicians who are followingthe directives of the EO-chosen experiment specification. In a mostpreferred embodiment step 413 is fully automated through a fullyautomated robotic laboratory with access to the complete range ofexperimental materials and/or material libraries and roboticexperimental equipment needed to execute the EO-Chosen. Step 413includes numerous sub-steps that are detailed within the EO-Chosensoftware object, including, without limitation, experiment scheduling,experiment sharing, charging, accounting, sequencing, collecting dataand storing data to an EO-Chosen.DATA.OUT file.

At step 414 the Experiment Controller passes at least oneEO-Chosen.DATA.OUT file to the Data Analysis Engine (DAE). At step 415,the DAE processes the data according to instructions, rules and/orparameter codes (including the USG-PCs) passed from the QM, and/orpassed from the EO-Chosen's data-processing-instruction (DPI) data,and/or passed from the Ex-CH in a DPI field of the DATA.OUTtransmission, and/or additional data-processing-instructions and/ordata-processing-rules held by the DAE's own DPI libraries.

Referring now to FIG. 4B, at step 416, which continues from theprogression of steps 413, 414 and 415 described above, the DAE caninclude substeps of characterizing the studied system using systemreverse-engineering analysis steps that find and/or generate behaviorrules and define normal relations of parameters based on prior knowledgeand the new information in the DAT.OUT file. The USG and QM can includedirectives that optimize the translation of test results through thedata-processing step to provide processed data output (PDO) suitable asinput for the Congruence Module (CM). The LOPE (including its EOs) canspecific inputs and provide directives for the DAE, including specifyingparameters (or variables) that will be processed in a data mining step.Each EO in the LOPE has DAE interoperability parameters. These can bepart of any number of standard experiment data processinginteroperability parameters, such as are provided by those skilled inthe relevant art, for example the MIAME, MAGE, BIRN methods and others,(see above). Similarly, the USG interface and/or the Query Manager canspecify inputs and provide directives for the DAE, including specifyingparameters (or variables) that will be processed in a data mining step.An optional step 421 can operate to evaluate the data sufficiency forthe intended data mining operation within the DAE, where failure at thisstep can lead to returning program control to the User Interface toadjust the setting of the goal and associated target parameters.

The substeps within the DAE step 416 can include data filtering, datanormalization, statistical analyses, hierarchical clustering, principalcomponent analysis, regression analysis, correlation analysis, supportvector machines, neural network analysis and any number of a range ofdata processing techniques called for by the Exp-Chosen object, theExp-Chamber, the USG, the QM, the Congruence Model, or the DAE itself.Substeps of the DAE step 416 can include fault-tolerant error-checkingroutines with corrective restart and secondary analysis pathways in theevent that a data-processing error is detected. Substeps of the DAE step416 can include numerous stages of checking for data completeness anddata sufficiency in the DATA.OUT file passed from the Ex-Chamber. TheDAE substep for reverse-engineering can be sequenced subsequent tovarious data mining steps or in interactive association with data-miningalgorithms.

At step 417, Processed Data Output (PDO), which can include results ofthe reverse-engineering update of a system causal network, is passed tothe Congruence Module (CM). In step 418 the Congruence Module completesupdating of the prior knowledge model for the appropriate SS domain(some of which updating may have already occurred in areverse-engineering DAE step) and in step 419 the CM compares the priorknowledge bases with the updated knowledge base for the currentiteration of the ARS. The updating of the knowledge base (or knowledgemodel, or knowledge assembly) can include accessing additional librariesof information and/or data from distributed data sources that lieoutside the ARS that can be related to new information provided throughsteps 414 and 416, inter alia. As depicted in FIG. 4B, step 419 caninclude an iterative process of mapping, overlay, testing, matching,solving and otherwise learning with regard to the congruence of newinformation relative to the prior knowledge model. Here, a number oftechniques that are known to those skilled in the art can be applied.

At step 419, the Congruence Module is continuously evaluating theimprovement in overall logical strength of the evolving knowledge model,based on metrics that are part of the CM testing library and/or othermetrics that can be supplied by the USG and QM, as well as metrics thatcan be derived from distributed library source. For instance, “richness”and “concordance” are metrics that are used by the library resource ofGenstruct Inc. (Cambridge, Mass.), whereas other measures of robustnesscan be created based on increase in VOI of the knowledge base foranswering simulated hypotheses or closing the information gap (IG) withthe goals of the USG. An information gap can be measured during the step419, where certain target information at some degree of certainty is setas one of the goals in the USG. These goals can include reducinguncertainty in a parameter, or detecting a previously unknownrelationship association between at least two parameters, or determininga normal range of related parameter behavior through one or more timesteps, or measuring any output of one parameter based on changing ofcertain inputs and/or experimental conditions or procedures. TheCongruence Module step 419 can be goal-directed to reduce one or morespecified information gaps (IGs).

When at step 419, or upon completion of certain substeps for testingreduction of IGs, the ARS determines that an IG has been reduced beyonda required specification in the USG, then the program produces acompletion report (CompRep) that describes the experiment conducted, theDAE steps achieved, the results of the Congruence Module testing, theclosure of the IG and any other reporting data called for by the USG,and delivers the CompRep to the user and the program terminates.

If at step 419, the Congruence Module procedures and testing fail toclose the IG as specified by the USG and the QM, then the CongruenceModule, at step 420, passes an IG report to the Experiment Director,which updates the USG-PC list, updates the ARS loop stage and updatesthe data for any relevant parameters in the Experiment Chooser RuleLibrary and/or the Experiment Usage Engine. At this point the ARS beginsanother cycle of operation corresponding to step 403 and 405 (see FIG.4A description).

The Congruence Module results can include multiple target unknownsgenerated by the DAE and additional learning steps of the congruencetesting and update of the knowledge base. These multiple new statementsof unknown relationship relevant to the closure of an originallyspecified USG can spawn new sub-goals and USG-PCs, which can beinstanced in multiple, parallel processes through subsequent loops ofthe ARS.

It should be noted that the original USG can be parsed by the ExperimentDirector into any number of multiple experimental pathways, such that asingle user query could spawn dozens or hundreds of experiments at thedirection of the Experiment Director, with scheduling and directiontoward available resources being constrained by USG-PCs related to time,cost, safety, resources, etc., and with the partitioning of tasks beingmanaged by a Multiple Experiment Manager, Scheduler and Sequencer Modulethat operates to optimize the rate of increase in useful experimentalinformation within the set constraints.

The ARS of an embodiment of the invention can iteratively study numerousexamples of normal and abnormal systems or system behaviors and/orsubsystems or subsystem behaviors to build a library of normal function(or behavior, or operations) and/or a library of dysfunctional(abnormal) functions (or behavior, or operations).

User Interface Goal Library

The ARS according to at least one embodiment of the invention caninclude the capability to address many different system types, where thetype or category of studied system (SS) can be selected by the user viathe User Interface (UI) that provides the user access to a library ofpossible studied systems and possible research goals for each of thesepossible studied systems. For example, without limitation, the GoalLibrary (GL) could contain the following list of systems for possiblestudy by the ARS:

Studied System Type 1 (SS-1): Virtual System; 2-dimensional grid

-   -   (including, for example an SS subtype of 4×4 grid with 16        locational squares, two components: {A,B})    -   Type 1 research goals:    -   (a) Test/observe to characterize normal    -   (b) Test/observe to detect abnormal (can be same set of        experiments as (a), or close)    -   (c) test changes to system to correct behavior    -   (d) Optimize corrective strategy        Studied System Type 2 (SS-2): Environmental System    -   (including, for example, the Global Climate System);    -   Type 2 research goals:    -   (a) Observe to characterize normal    -   (b) Detect abnormal    -   (c) Test changes to correct system behavior    -   (d) Test adaptive strategies    -   (e) Optimize corrective and/or adaptive strategies        Studied System Type 3 (SS-3): Computer Program/Hardware System    -   Type 3 research goals:    -   (a) Test to characterize behavior    -   (b) Detect bugs    -   (c) Test changes to correct bugs    -   (d) Optimize        Studied System Type 4 (SS-4): Electrical System    -   Type 4 research goals:    -   (a) Characterize normal    -   (b) Detect/characterize abnormal    -   (c) Test corrective designs    -   (d) Optimize among corrective designs        Studied System Type 5 (SS-5): Information System        Studied System Type 6 (SS-6): Social Organization or Group:

(The research goal and EOs in the LOPE for this domain of studiedsystems (SS) can include an EO that uses an artificial intelligencemodule in the EO that puts information onto the World Wide Web (such as,for example, through blogs) and measures responses (such as, forexample, by page views, view duration, entered responses, inter alia),with the EOs in this SS domain further including instructions for theDAE to data mine and analyze the resulting data to observe, filter,categorize and/or sort instances and parameter responses per eachinstance and/or to further establish and describe normal and abnormalresponses within the studied system to this experiment. Theseexperiments can produce results useful to studies of politicalconstituency attitudes or behavior, consumer product marketing attitudesor behavior, or media marketing effectiveness)

Studied System Type 7 (SS-7): Industrial Sub-sector

Studied System Type 8 (SS-8): Living Organism

-   -   Type 8 research goals:    -   (a) Characterize normal function    -   (b) Detect/characterize abnormal function    -   (c) Test/find corrective strategies    -   (d) Optimize among corrective strategies        Parameter Codes

The series of parameters (codes) that can be used to specify anexperiment within the Library of Possible Experiments (LOPE) can includeparameters (P-#) for such things as experimental stage (1) (for example,P1:1 can refer to the very first round of learning by the ARS inresponse to a USG, with no prior knowledge in the knowledge base, onlyspecification of the SS domain; whereas P1:47 might refer to an ARSoperation whose stage is currently in loop 47 of an experimentaliteration on the path of a particular USG. Similarly, other parametercodes can be utilized, such as, without limitation:

-   -   P2—Safety    -   P3—resolution    -   P4—Subsystem type    -   P5—Subsystem scope    -   P6—Scope    -   P7—Cost/Budget    -   P7—Time/Deadline    -   P8—Robustness Required    -   P9—Regulatory    -   P10—Intellectual Property

The method and system of at least one preferred embodiment of theinvention can be better understood and illustrated by simple examples,following below. It will be understood, however, that the scope of theautomated research system provided by the invention reaches to includemuch more complicated systems and examples of automated research thatthose skilled in the art can implement by extrapolating from thedescription and examples of the invention provided herein.

Example 2. Simple 2-D Matrix System

[System SS-1, containing component A and component B]. Take a simplesystem of at least two interacting subsystems A and B.

Referring to FIG. 5A-5D, corresponding to an experiment measured atthree time steps and at an end point, respectively, for a simple systemof two components, A and B, an observation of system function may showthat the two components migrate into the inner box and remaining withinthat region in a balanced, ongoing association (which, for example, inthe case of a biological cellular system, could correspond to twobiological molecular constituents migrating into and remaining withinthe nucleus of the cell). Repeated observation of system behaviorthrough multiple time points, from initial conditions to an end point,could establish that this migration and continued association within abounded sub-region of the system is a rule of normal system function forthis studied system, SS-AB. A statistical distribution of positions ateach time point, t(o)-t(n), may be found to follow a normal Gaussiandistribution of configurations for each time point, such that a “normal”behavior of the system over all the time points could be considered aprogression through any of a normal set of positions for any time point.Each time point could have a normal distribution of potentialconfigurations, with some configurations more probable than others, withthe probable normal behavior defined by marking some degree of deviation(e.g., some degree of sigma) from a center of the normal distribution.Conversely, an abnormal behavior of the system could be observed in anexperimental run, with “abnormal” defined as a behavior that at one ormore time points displays a configuration that is not within a specifieddeviation from the center of the normal population of configurations forthat time point. For example, for a simple system, SS-AB, having anormal rule of reaching an endpoint with component ‘A’ and ‘B’ inbalanced association within a sub-region (as shown in FIG. 5A-5D), anexperiment could detect a behavior such as shown in FIG. 6A-6D or abehavior such as shown in FIG. 7A-7D. In the experiment having resultsshown in FIG. 6A-6D, for example, component ‘A’ never enters thesub-region. In FIG. 7A-7D, both components enter the central sub-region,but component ‘B’ doubles while component A disappears.Experiment Director

An ARS according to a preferred embodiment of the invention can have anExperiment Director module (ED), which can interface with and interactwith a Library of Possible Experiments (LOPE), wherein the LOPE can be apart of the ARS. For instance, returning to the very simple studiedsystem SS-AB described in FIGS. 5A-5D, a LOPE can include the followingthree experiments, inter alia:

SS-AB-Exp.#1: Initiate A+B system with A(x,y) and B(x,y) at t(0)specified as A(1,1) and B(4,4). Complete measurements at t(1), t(2) andt(3). Observe positions and record to SS-AB-E1.data.out. FIGS. 5A-5D canbe seen to be the observed data that could be the result of one run ofthis experiment, whereas FIGS. 6A-6D and FIGS. 7A-7D would be additionaldata for additional runs of this experiment.SS-AB-Exp.#2: Build the system as in Exp #1, but create a series ofMonte Carlo instantiations with twenty random starting positions ofA(x,y) and B(x,y). Complete four time steps for each of the twenty runs.Observe each time step and record to SS-AB-E2.data.out.SS-AB-Exp.#3: Create random experimental start, constrained to 100instances (runs) and ten time steps per run. Observe each time step andrecord data to SS-AB-E3.data.out.

A Value of Information (VOI) index can be created for each of thepossible experiments in the LOPE, where the values can be compared as arelative percentage (most valuable is 100%), for example:

Experiment VOI SS-AB-Exp.#1 5% SS-AB-Exp.#2 40%  SS-AB-Exp.#3 60% 

An Experiment in the Library of Possible Experiments can be generallytermed an Experiment Object (EO). An Experiment Object can be describedas a software object and/or as an information object within the ARSgenerally. The EO can be a technique described in text and/or graphicform, or a series of techniques, methods, operational steps and/or othermanipulations that can be understood to comprise an experiment, or thatcan be characterized as measuring, detecting, studying, observing,perturbing or otherwise sensing state or change in one or moreparameters, factors or variables in a studied system. The EO can existas a software object and/or as a menu in an encyclopedia of experimentaltechniques.

In one embodiment of the invention the ARS can include a LOPE thatcontains at least two EOs as software objects, wherein the EOs includeinformation about the conduct of the experiment, the required inputs,the likely data outputs, “private” object data required for successfuldirection of the experiment procedures when run out through the Exp.Director (such as, for example, when the ED directs a virtual experimentand/or directs a series of robotic experiments), “public” data that canbe shared with other components of the system at any time, and otherinformation concerning the experiment, such as the VOI index information(calculated and/or based on prior experimental usage), cost information,location information, intellectual property ownership aspects of theexperimental methods or materials used in the experiment, intellectualproperty claims in the experimental results, experiment sequencinginformation, information on safety and safety procedures, information onregulatory and compliance requirements and procedural documentationsteps, time requirements, allowed experiment variations, preferred SSdomains for experiment application, experiment input requirements,experiment prohibitions, uncertainty information as to process andoutcome and any other information that can be used to evaluate thesuitability of the experiment for progressing toward the user-specifiedgoal.

Thus, in the foregoing example of an ARS according to the invention forstudying a simple SS-AB, the LOPE can contain SS-AB-EXP #1-3, and thesecan be stored as software objects, wherein the software objects can beaccessed by the ED to direct any one of the experiments and where eachof the EOs contains self-referential descriptive data, such as, forexample, VOI data, that can be used to choose which experiment to applyat a given time to make progress toward the user-specified goal (USG).In the above example, for instance, Exp #3, having a higher VOL owing tothe greater amount of data that the experiment would acquire, could beevaluated by an Experiment Chooser module as a more preferentialexperiment to run to gain information.

In one embodiment of the invention, the invention provides for an ARS inwhich success metrics and/or value of information gained from theresults of an experiment that has previously been run by the same ARS(or by a 3^(rd) party or 3^(rd) party's research system) is summarizedat least as to category and success and or VOI scores, with a stepincluded to update the EO in the LOPE using this summary information,with the updated VOI information being aggregated into the VOI metricheld by the EO in its self-referential data store.

Experiment Usage Engine (EUE)

In addition, an Experiment Usage Engine (EUE) can be included in the ARSaccording to at least one embodiment of the invention, wherein the EUEis a software module that interfaces with the Experiment Chooser and theLOPE and can include a set of conditional rules and/or rule evaluationsteps that create a ranking of preferential application of one or moreexperiments to an Information Gap (IG) challenge (or information need,according to the USG). As described above, various rules of applicationfor any experiment can be included as part of the EO itself, specifiedby the creator of the experimental technique, method or menu, or by theprovider of the experimental service (such as, for example, a providinglaboratory object) and/or the experiment usage rules can be assembled asan evaluation set within the EUE. An example of an EUE evaluation setcan be as follows, in the context of the simple SS-AB research domain:

Experiment Usage Rules SS-AB-Exp.#1 If no prior information, then useExp#1 If budget <100, then use Exp#1 If research loop iteration >100,then do not use Exp#1 If robustness requirement >50, then do not useExp#1 SS-AB-Exp.#2 If budget <300 and >100 units, and if robustnessrequired >50 then use Exp#2 SS-AB-Exp.#3 If budget >300 units, and ifrobustness required >85 then use Exp#3Investigating Biological Network Dynamics and Automated ExperimentalLoops: Applying RDF/OWL

Embodiments of the invention further provide technology for meeting thechallenge in biomedical research to evaluate results of gene expressionexperiments in the context of prior knowledge. One approach is to (a)analyze gene expression data to a first step of a reduced set ofseemingly important genes that exhibit correlated behavior, (b)reverse-engineer from these data a probable network or set of networkswithout regard to prior knowledge, and then (c) attempt to make sense ofthe experimental result against a backdrop of pathways maps derived fromcuration and analysis of the biomedical literature.

Another approach is to utilize the reduced set of correlated genes fromthe gene expression data as a query to a knowledge base that is formedfrom the literature utilizing a myriad of bioinformatics tools,extracting a network or set of networks from the knowledge base formedin response to the query set. The first approach above offers theadvantage, if done well and based on sufficient experimental design, toshed light on unknown unknowns, but suffers from the weakness of highuncertainty owing to uncontrolled variables. The second approach aboveis less likely to correct prior ignorance and error, but is more likelyto generate a molecular network that sits robustly on an assembly ofmany prior lab experiments.

A preferred embodiment of the invention provides for combining these twoabove approaches, leveraging their strengths and minimizing theirweaknesses. In addition, the invention provides for automating thegeneration of hypotheses and the design of iterative gene expressionexperiments that can benefit the pace of discovery.

Forward simulation is used in both the above approaches as part ofderiving best fits between early guesses at a network and a conclusionabout which derived network deserves to be considered more probable.Simulation of discrete logical cascading steps without concern for timesequence can provide some information about causation sufficient togenerate hypotheses, but may provide little information about mechanismdetails. Modeling continuous signal changes in expression levels, withexplicit treatment of time dynamics, can have a chance of allowingdistinction between specific mechanistic pathways, including nonlinearresponses and feedbacks.

To utilize RDF/OWL features in the effort to merge the above approachesto discover biological function the invention addresses a number oftechnical problems, including:

1. Time: The invention provides for creating standard approaches formodeling dynamics and time-based functions and coping with curatedpathways (and/or causal networks) that have little or no dynamicinformation;

2. Spatial context: An embodiment provides for modeling spatial andsystem context, in the sense that there are numerous levels ofself-organization requiring nested dynamic modeling in the forwardsimulation of molecular assemblages, cells, tissues, and metabolicsystems, among others;

3. Fluid interactions: Given that mammalian biology proceeds to a largeextent as a function of aqueous chemistry, an embodiment of theinvention provides for modeling concentration, diffusion, pH, redoxpotential, ionic dissociation, and bulk transport in the modelingmodule;

4. Energetics: Energy parameters (as well as material balances) canprovide important parameters for constraining a dynamic simulationmodel, including temperature, Gibbs free energy, enthalpy, entropy andother thermodynamics variables as well as energy represented inelectrical potential and phosphate exchanges. Embodiments provide fordeveloping ontologies for one or more of these thermodynamic functionsand interrelationships;

5. Topology and Congruence testing: An embodiment provides for comparingcausal networks topologically as an important method for rapidlybringing experiment-derived networks and literature-derived pathwaysinto focus, highlighting match-ups and inconsistencies and determininginformation gaps that must be filled to meet a user-specified researchgoal. The invention provides useful standards for carrying topologicaldescriptors forward with reporting of pathway relationships; and

6. Scenarios and Experimental Templates: An embodiment of the inventionprovides for an intelligent system that can propose an experimentaldesign based on a generated hypothesis, a library of possibleexperimental approaches and/or scenarios must be available, with alogical structure that has sufficient flexibility for working betweengenes, proteins and metabolites, yet enough exact specificity to directa robotic process.

Specific features can be included in the invention that take advantageof the Internet standard (Semantic Web) methods called RDF/OWIJLSID.Other features can be integrated with SBML, UML and other dynamicmodeling standards known to programmers having ordinary skill in theart.

Example 3. Toxicity

Embodiments of the invention can provide a method to gain insight aboutmolecular network interactions and metabolic response patternsassociated with a toxic dose of Compound X to a biological sample. Datamining (such as with the SLAM algorithm in the GeneLinker Platinum™software, Improved Outcome Software Inc., Kingston, Ontario, Canada) canbe used to detect biomarkers and reverse engineering methodologies (suchas the Integrated Bayesian inference System (IBIS) andreverse-engineering algorithm-linear (REAL) methods developed byBiosystemix, Kingston, Ontario) are used to gain insight into biologicalnetwork interactions. The method then provides for:

1. Building further insight on potential toxicity by uncovering hiddenrelationships in “pan-omic” data sets and unique responses thatcorrelate with treatment;

2. Identifying biomarkers of key outcomes from treatment of Compound X;and

3. Inferring the gene regulatory network imputed in and allowingprediction about dose-response outcomes of the particular compound.

These steps can be accomplished by utilizing databases of curatedbiomedical literature, such as those compiled by GeneGo, Inc. (St.Joseph, Mich.), Ingenuity, Inc. (Redwood City, Calif.) and/or Genstruct,Inc. (Cambridge, Mass.), inter alia, including data sets comprising:

-   -   Rat RNA samples    -   Response to Compound X:        -   i. 1 drug treatments—high dose (toxic)        -   ii. 1 drug treatments—low dose (non-toxic)        -   iii. 1 vehicle treatment    -   3-4 post treatment time points    -   5-10 replicates per treatment group    -   8,000 gene Affy rat array (U34A); 75% known and 25% ESTs    -   Proteomics on Serum (2D Gel and SELDI)    -   Metabolomic data on urine (spectral)    -   Pathology and histopathology scoring (0-3)

The analysis method can then comprise the further steps ofclassification and identification of biomarkers, such as, for example,the following steps and substeps:

-   -   1. Identify sets of genes, proteins and metabolomic variables        that accurately and robustly classify specific compound        response, phases of the response, and outcomes in terms of        histological and pathological data:        -   a. Solve classification problems to assure comprehensive            coverage of predictive genes, proteins and metabolomic            variables.        -   b. Consider associations between genes, proteins and            metabolomic variables and outcomes within the same            measurement time point, and across time points, to capture            potential inductive effects.        -   c. Identify distinct gene and protein expression and            metabolomic variable profiles (markers) for adverse effects            and for efficacy.        -   d. Investigate associations between compound response,            phases of response, and phenotypic outcomes.        -   e. Statistically validate the classification results.    -   2. Integrate biomarker genes and/or proteins identified in        current experimental results using nonlinear and combinatorial        methods with biomarkers, such as, for example, those known or        found earlier by GeneGo Inc. or Genstruct Inc. (or other        knowledge assembly analysts, or known in the KBs or in the        medical research literature).

The analysis method can then comprise the additional steps of reverseengineering and mapping causal network Interactions. In order todetermine regulatory relationships that control key biomarkers, asidentified in Stage 1(A), above, the method according to an embodimentof the invention can include the steps of:

-   -   1. Applying linear and nonlinear gene network reverse        engineering methods to identify key influence genes, proteins        and metabolomic variables; and    -   2. Reverse engineering a sufficient number of connections to        allow reasonably robust simulations to probe hypotheses on        therapeutic intervention effects.

Wherein, the output from the above steps in a preferred embodiment caninclude:

-   -   1. A listed subset of biologically relevant genes, proteins and        metabolomic variables based on uncovering hidden relationships        and unique and/or differential responses; and/or    -   2. Network and pathway interactions that regulate those genes,        proteins and metabolomic variables with key influence on        biological response.

Example 4. Knowledge-Base Assembly Function in Biomedical Research

Referring to FIG. 8A-8E, in an automated research system according to anembodiment of the invention, a knowledge base assembly function caninclude a combination of functions and interactive steps between adata-driven, reverse engineering module 800, a 3^(rd)-party,literature-based, pathway assembly module 801, a congruence module 803and a simulation module 802.

As seen in FIG. 8A, the reverse-engineering module function 800 appliedto time series measurements of system variables includes completingstatistical analysis steps 804 and then completing association-miningsteps 805, to produce predictor-set output 806 for use in causal networkanalysis. Continuing from the association mining into further networkreverse engineering analysis 807 can produce a reverse-engineeredpathway network (REPN) model 808 that is based solely on probablecausality based on associations between a set of variables, i.e,independent of prior knowledge from a knowledge base. A pathway assemblyfunction 801 can proceed from a domain knowledge base developed from3′-party literature sources, such as biomedical research publicationsrepresenting millions of prior experiments conducted over many decades.The assembly of causal network within the knowledge base can include atext-mining step 809, development of one or more ontologies in step 810and pathway mapping steps 811 which steps can combine to form a pathwaydatabase and network (PD&N) map 812 based on the prior knowledge in theknowledge base.

In the congruence module 803 a series of comparisons between the pathwaydatabase and network map 812 and the reverse-engineered pathway networkmodel 808 can be conducted to determine whether or not thereverse-engineering of the experimental result reproduces the priorknowledge of the network, or whether there is a gap. The comparison canalso reveal whether or not the experiment produced new information thefills in an unknown area of the prior network map. An in silicosimulation step 816 can be conducted in conjunction with the convergingof the pathway database and network (PD&N) map 812 andreverse-engineered pathway network (REPN) model 808 to detectimprovements in how well the system is understood, on the assumptionthat improvements in understanding the system will lead to simulationsthat more closely approximate the outcomes of actual experiments. InFIG. 8A, a first comparison step at 813 can be tested for congruencybetween the PD&N map 812 and REPN model 808, where increase incongruency corresponds to increasing the matching overlap of the twopathway networks. Learning from a simulation step and degree of mismatchseen in congruence-test 813 between the PD&N map 812 and REPN model 808can cause an updating from the Congruence Module 803 to both theknowledge base network map 812 and to the reverse-engineered networkmodel 808. Following these updates, a second congruence test 814 isconducted, again exploring the converging of the PD&N map 812 and REPNmodel 808, with again the converged pathway model being tested in asimulation 816. As seen in FIG. 8E the simulation 816 can includedynamics and flux analysis 847, exploration of robustness and noisesensitivity 848, in silico knockout and constitutive overexpressiontesting (in gene expression networks) 849, and/or combinatorialperturbation analysis 850. Further updates can occur and furtheriterations of fitting reverse-engineered model to the knowledge-base mapcan be conducted 815.

FIG. 8B illustrates that statistical analyses 804 can comprise examiningreplicates 819, selecting gene and protein sets 829 and statisticalfiltering 821, and that the association mining function can includeapplying sub-linear association mining (SLAM) 822 to select highlyinformative patterns (association sets), applying Bayesian inference 823to select outcome predictive genes and markers, and assemblingoutcome-predictor sets of variables 824.

FIG. 8C illustrates that network reverse engineering functions 807 caninclude steps of initial exploration with a reverse-engineering linearalgorithm (REAL™, as described by Biosystemix Ltd., Kingston, Ontario)826, identifying novel pathway candidates 827, graphing and reviewingthe imputed control/causal structure 828, constructing a network graph829, graphing major regulatory nodes 830, estimating functionalinferences of pathways 831, exploring dynamic pathway/network controlthrough flux analysis 832, highlighting specific gene or proteincontributions per experiment dose treatment 833, analyzing fornon-linear dynamic networks 834 and applying further Bayesian approaches835 to merge the previous analyses and estimations in a pathway networkmodel.

FIG. 8D illustrates that the text-mining step 809 can includeaggregation 839, reading in the text of an article 840, parsing the readtext 841, auto-assembling an XML version 842 and loading into a database843. The ontology development 810 can include a Sort/Sift step 844whereby objects (nouns), interactions (verbs) are sorted based oncontext with meta-data updated and synonyms analyzed to resolveconflicts. The pathway mapping step 811 can include an auto-assemblystep 845 wherein the object/nouns are mapped as graphic nodes, theinteraction/verbs are mapped as graphic arcs, and system and subsystemscaling is adjusted based on context.

Example 5. Automated Biomedical Research System

An embodiment provides for an Automated Biomedical Research System(ABRS) that can include a commercial module that can incorporate marketeconomic analyses and approaches that can be combined into the researchsystem to enhance the KB as well as the UI, QM and ExpDir functionality.Commercial and/or market inputs to the system can include market data ondiseases, incidence, cost of disease, cost of treatment, duration ofdisease, duration of treatment, mortality, market positioning, FTO andIP positions, royalty requirements, competition, potential customers,customer budgets, sales cycles, phases costs, delay costs, budgeting perschedule considerations, cross-investment, ROI, contract requirementsand other legal issues, risk factors, and other commercial and/ormarketing factors.

Additional components of the ABRS system can include one or more of thefollowing system elements:

-   -   (1) Computer System, with software operating system component    -   (2) User interface (UI) module and visualization component    -   (3) Query Manager module (with the UI generates user-specified        goal (USG) instruction or directive)    -   (4) Database and Knowledge Base (KB) module        -   (a) Domain literature Pathways component        -   (b) Domain Manual Ontology component    -   (5) Experiment Director module        -   (a) Experiment Chooser component        -   (b) Sourcing decision component        -   (c) Experiment Controller component    -   (6) Data Processing Module        -   (a) Filtering component        -   (b) Image processing component    -   (7) Data Analysis Engine        -   (a) Data Mining component    -   (8) Modeling module        -   (a) Knowledge Base Assembly Core Component        -   (b) Simulation Component        -   (c) Reverse-engineering component    -   (9) Congruence Module    -   (10) Simulation Module    -   (11) Commercial module        -   (a) Business development transaction component (templates,            forms, contact management, account management, RFPs,            proposals, etc.)        -   (b) Market analysis component        -   (c) Sales/Marketing component (e.g., targets, quantities,            timing, price points, etc., interfacing with commercial and            Query Manager modules)        -   (d) Legal component (IP, royalties, contracts, licensing,            etc.)        -   (e) Financial component (budgeting, risk analysis, cost            analysis, etc.)    -   (12) Quality-control (fault tolerance) module        Experiment Usage Rule Engine and Ontology Methods

Inputs to the Experiment Usage Rule Engine can be stored using methodsof building ontologies, such as XML, OWL, CORBA and other methods ofsoftware object and information object creation for use acrossdistributed networks and/or within relational database structures. Forinstance, a general experimental procedure can be described in anynumber of approaches that are known to those skilled in the art ofcommon ontologies and controlled vocabularies to enable data exchangefor experiments, such as, for example, ontologies in the biosciences,which can be found by investigating:

-   -   Human Proteome Organization Proteome Standards Initiative        standards for data transfer and deposition. These standards        utilize ontologies and controlled vocabularies to describe        experimental procedures and common processes such as sample        preparation, such as the GO ontology and including nomenclature        of the world's leading protein sequence database, UniProt, while        incorporating and adding to the GO annotation of molecules        described within UniProt-Swiss-Prot and UniProt-TrEMBL, also has        its own defined keyword section that allows users to perform        searches across the database using a standard nomenclature        consistent to all entries;    -   A number of both commercial and academic molecular interaction        databases that exist (IntAct, BIND, DIP, MINT, Hybrigenics,        HPRD, MIPS) wholly or partially in the public domain; and    -   The HUPO-PSI MI format that has been developed using a        multi-level approach similar to that used by the Systems Biology        Markup Language (SBML). Level 1, published early in 2004.

Ontologies can be integrated with the knowledge-base components of theinvention by one having ordinary skill in the art, with guidance fromthe methods disclosed by “The Use of Common Ontologies and ControlledVocabularies to Enable Data Exchange and Deposition for ComplexProteomic Experiments S. Orchard, L. Montecchi-Palazzi, H. Hermjakob,and R. Apweiler; Pacific Symposium on Biocomputing 10:186-196 (2005),hereby incorporated by reference herein in its entirety;http://helix-web.stanford.edu/psb05/orchard.pdf.

Several controlled vocabularies have been developed, includinginteraction type, feature type, feature detection method, participantdetection method, and interaction detection method to describe specificaspects of both an interaction and the experimental methodology used todetermine these, such as, for example:

-   -   “Minimum Information About a Proteomics Experiment (MIAPE)”        document analogous to the MIAME requirements for a micro-array        experiment, and both an object model (PSI-OM) and XML format        (PSI-ML) to fully represent a proteomics experiment. PSI-GPS        uses the modules such as the more specific mzDataformat as        components of a full experiment description, comprising sample        preparation, analysis technologies, and results. To delineate        these processes, controlled vocabularies are written and        appropriate terms contributed to the MGED Extended ontology        under the “PSI” namespace. The MGED ontology is written to        support the micro-array object model, MAGE. The extended version        adds further associations and classes to the core ontology which        is intended to be stable and fully in synch with MAGE.    -   National Center for Biomedical Ontology's BioPortal. BioPortal        is a Web-based application for accessing and sharing biomedical        ontologies.    -   Biomedical Informatics Research Network (BIRN) is a        geographically distributed virtual community of shared resources        offering tremendous potential to advance the diagnosis and        treatment of disease. BIRN enhances the scientific discoveries        of biomedical scientists and clinical researchers across        research disciplines.

Features in BioPortal 2.0 include the XCEDE schema, which provides anextensive metadata hierarchy for describing and documenting research andclinical studies. The schema organizes information into five generalhierarchical levels:

-   -   1. a complete project;    -   2. studies within a project;    -   3. subjects involved in the studies;    -   4. visits for each of the subjects; and    -   5. the full description of the subject's participation during        each visit.

Each of these sub-schemas is composed of information relevant to thataspect of an experiment and can be stored in separate XML files orspliced into one large file allowing for the XML data to be stored in ahierarchical directory structure along with the primary data. Eachsub-schema also allows for the storage of data provenance informationallowing for a traceable record of processing and/or changes to theunderlying data. Additionally, the sub-schemas contain support forderived statistical data in the form of human imaging activation mapsand simple statistical value lists.

XCEDE was originally designed in the context of neuroimaging studies andcomplements the Biomedical Informatics Research Network (BIRN) HumanImaging Database, an extensible database and intuitive web-based userinterface for the management, discovery, retrieval, and analysis ofclinical and brain imaging data. This close coupling allows for aninterchangeable source-sink relationship between the database and theXML files, which can be used for the import/export of data to/from thedatabase, the standardized transport and interchange of experimentaldata, the local storage of experimental information within datacollections, and human and machine readable description of the actualdata.

To facilitate the use of the XCEDE schema, a toolbox has also beendeveloped based on XCEDE for the storage of neuro-imaging activationmaps and anatomical labels. Also see: Astakhov V, A Gupta, J Grethe, ERoss, D Little, A Yilmaz, M Martone, X Qian, S Santini, M Ellisman (inpress) Semantically Based Data Integration Environment for BiomedicalResearch. Proceedings of the 19th IEEE International Symposium onComputer-Based Medical Systems, in press. incorporated by referenceherein in its entirety; Astakhov, V, A Gupta, S Santini and J S Grethe(2005) Data Integration in the Biomedical Informatics Research Network(BIRN), In: (B. Ludäscher, and L Raschid eds.) Second InternationalWorkshop, Data Integration in Life Sciences, San Diego, Calif., USA,Jul. 20-22, 2005. Proceedings. Lecture Notes in Computer Science:3615:317; incorporated by reference herein in its entirety; and Grethe JS, Baru C, Gupta A, James M, Ludaescher B, Martone M E, Papadopoulos PM, Peltier S T, Tajasekar A, Santini S, Zaslavsky I N, and Ellisman M H(2005) Biomedical Informatics Research Network: Building a NationalCollaboratory to Hasten the Derivation of New Understanding andTreatment of Disease, Stud Health Technol Inform. 2005; 112:100-9;incorporated by reference herein in its entirety.

Example 6. 2-D Matrix—Type-1 Domain

User starts ARS program by turning on computer and opening the UserInterface:

“Run ARS User Interface”

User chooses a Studied System Type from a standard pull-down menudisplayed by the UI. In this example:

“Choose Studied System: Type 1, (ST-1)”

“If SS Type=ST-1, then load ontology ‘ST-1: Virtual System;2-dimensional grid’, including terminology, rules and/or guidelines fileor files for this SS domain (or software objects, which can includedynamic software sub-routines)”

-   -   (Here the domain ontology, rules and guidelines can be loaded        into memory to provide the necessary terminology and a number of        rules, principles, parameters, guidelines and/or other        information to be drawn upon by the User Interface module, the        Query Manager module, the Experiment Director module (including        the Experiment Chooser module when choosing the preferred        initial experiment), the Experiment Chamber, the DAE and the        Congruence and Goal Completion testing modules in subsequent        stages of the research process. In addition, or alternatively,        the domain information that is loaded can include software        objects that can have additional dynamic program capability to        interact with the Query manager of the ARS and/or the User        Interface and/or other modules of the ARS).

“Choose Studied System SubType=Chess”

-   -   (Here the User Interface can have subtypes in its data-store or        can access a list of subtypes from the domain ontology accessed        in the previous step).

“If SS Type=CHESS, then load ‘Chess Manual’ ontology, rules and/orguidelines file or files (or software objects, which can include dynamicsoftware sub-routines)”

-   -   (Here further aspects of the domain ontology, rules and        guidelines as may be more appropriate to the chosen sub-type can        be loaded into memory to provide the necessary terminology and a        number of rules, principles, parameters, guidelines and/or other        information to be drawn upon by the User Interface module, the        Query manager, the Experiment Director module (including the        Experiment Chooser module when choosing the preferred initial        experiment), the Experiment Chamber, the DAE and the Congruence        and Goal Completion testing modules in subsequent stages of the        research process).

“Set Scope/Size: 5×5 board

“Set Starting setup or constraints: 5 Black Queens and 3 White Queens

“Set Research Goal Type(s): (a) test/find corrective and (b) optimize

“Set Goal-Subtype ‘(a) test/fin corrective’: Defensive PositionTest/Find

“Set ‘(b) optimization level’: 100% optimization

“Set Optimization definition or parameters: No threats”

-   -   (Here the User Interface can be using goal-setting guidelines        from a portion of the “Chess Manual” domain manual, such that        the manual can pass to the User Interface module the required        scoping queries for the user's responsive entry to create the        USG directive).

It will be appreciated that a User Interface module can be written toretrieve directly domain ontology information from anywhere on theInternet (or other network or electronic data pathway) and/or a QueryManager module can be provided that interfaces with the User Interfaceand other of the ARS modules and 3^(rd)-party information sources andthat assists in handling input-output queries and responses between theUser Interface and the one or more libraries of domain information (suchas, for example, domain libraries distributed on the Internet, that mayutilize XML, CORBA, OWL, DAML+OIL, RDF schema and/or other informationtechnologies).

These additional query formulations retrieved from the ontology and/orguideline files can be provided to the user through pull-down menus inthe User Interface. For example, the Chess Manual ontology can provideinformation that Test/Find experiments can include ‘Defensive Position’tests for which an optimization pathway can be selected as “No threats”between the black and white chess pieces.

The above goal specification now comprises the user-specified goal (USG)for one or more iterations of the ARS in this embodiment, where the SStype (and/or subtype) is the domain of chess (or, even morespecifically, the domain of a subset of a chess-board space, i.e., theregular 8×8 square board reduced to a 5×5 square board).

“Run Experiment Director,” which reads the USG file directive.

-   -   (Upon initiation, the ARS will pass the USG information to the        Experiment Director (either directly or via the Query Manager        module), which has methods and modules that can be further        illustrated here in pseudo-code, below, from which one skilled        in the art can program in a number of possible computer        languages and implement in a number of alternate combinations of        computer system and software that can be connected to an        experiment chamber by electronic communications, e.g., by the        Internet, LAN, WAN or other well-known means):

“USG passed to the Experiment Chooser module of the Experiment Directormodule:

“Experiment Chooser analysis:

“If SS subtype=5×5 board, then include constraints of the 5×5-boardsubset of the ‘Chess Manual’ rule and guideline file”

-   -   (Here a sub-domain of the Studied System can be matched to        include specific additional constraints, rules or principles        from subsections of the domain manual, or subsections of the        domain knowledge base)

“If Exp. Stage/Goal in the USG=Protective, 100% optimize, then loadPosition and Threat Analysis subset of EOs in the LOPE that pertain tothis SS-1-Chess subdomain.”

-   -   (Here, in this example, based on the USG directive, which can        include parameter codes declaring the goal of protective (no        threats) at 100% optimization, the Experiment Chooser leads to        selection of at least one Experiment Object from the LOPE for        this SS chess domain, as described below).

“If Start Constraint=Queen components, then load Queen behaviors”

-   -   (For this example, the starting constraint has been to load 5        Black Queens (BQ) and 3 White Queens (WQ), or to conduct        experiments with these components, which then leads to an        instruction to load from the knowledge base the known behaviors        and rules associated with these components, i.e., a Queen can        move or threaten any square in view along a column or a rank or        along a diagonal.)

“SELECT LIST and DESCRIPTION of POSSIBLE EXPERIMENTS from the LOPE andLOAD to Experiment Director/Experiment Chooser”

In this example, for illustration, the Experiment Chooser module loadsdescriptive information for three Experiment Objects categorized under“SS-1:Chess: 5×5 board-Queens-No-threat goal” that show in their dataand procedural description the ability to search for no-threatprotection:

“→SS-1:Chess: 5×5 board-Queens-No-threat goal: Exp.#1: 5 BQ, 3WQ . . .initial condition load components B1, B2, B3, B4, B5, W1, W2, W3 intoboard positions a5, b5, c5, d5, e5, a4, b4, c4, respectively. Evaluate,testing for threats”

-   -   (i.e., testing if any BQ and WQ component exist on same file        (column), same rank (row) or same diagonal).

“If TEST=YES, then modify positions by RULE 1.1, RULE 1.2 or RULE 1.3.If TEST=NO, then STOP and REPORT SUCCESS; VOI=Low; Timerequirement=High”

-   -   (See FIG. 9A as illustration of the starting position of        Experiment #1, where RULE 1.1 can be to increase spacing between        each component sequentially along the rows, which would produce        the pattern in FIG. 9B, or RULE 1.2 could specify changing        positions until maximum separation by rows between black and        white components, leading to FIG. 9C, or maximum separation by        diagonals, leading to FIG. 9D).

“→SS-1:Chess: 5×5 board-Queens-No-threat goal: Exp #2: 5BQ, 3WQ . . .initial condition set as random placement of all components. Test forthreats. IF TEST=YES, then modify positions by RULE 2.1 (restart). IfTEST=NO, then STOP and REPORT SUCCESS. VOI=Low; Time requirement=High

-   -   (See FIG. 9E-9H for illustrations of the progress of Experiment        #2, where RULE 2.1 is simply to reset the position by random        placement).

“→SS-1:Chess: 5×5 board-Queens-No-threat goal: Exp #3: Use knowledgebase of chess guidelines and principles to initiate placement. Startwith RULE 3.1—choose smaller of component sets, {WQ} versus {BQ},resulting in choice of {w}, and place WQ1 on weakest of queen positionsto yield maximum number of unthreatened squares”

-   -   (Chess manual shows FIG. 10A-10C, which results in choice of        FIG. 10C initial position, yielding 12 unthreatened squares.)

“CONTINUE. Place WQ2 on remaining weakest position, using chess manualprinciple that REDUNDANCY with coverage of WQ1 should be maximum and tomaximize remaining unthreatened squares at greater than or equal to 6unthreatened squares,”

-   -   (which can result in position of FIG. 10D).

“CONTINUE. Place WQ3 on remaining weakest position (which can result inposition of FIG. 10E), using chess manual principle that REDUNDANCY withcoverage of WQ1 should be maximum and to maximize remaining unthreatenedsquares at greater than or equal to 5 unthreatened squares. TEST forthreats. If TEST=YES, then report NO SOLUTION. IF TEST=NO, then STOP andREPORT SUCCESS (such as position of FIG. 10F).

-   -   (Note that FIGS. 10C-10F represent progress of Experiment #3. It        can be seen that the steps of RULE 3.1 can be programmed rather        easily by testing for unthreatened squares. In the final step        the routine need only search for the column and/or file having        only one open (unthreatened) square and test these two        possibilities (positions c1 and d3) for solution. Thus, this        Experimental Object converges very rapidly to a solution (i.e.,        toward meeting the USG).

“VOI=High; Time requirement=Low”

“(OPTIONAL) Evaluate VOI for each OE extracted from the LOPE”

“(OPTIONAL) Evaluate Cost, safety and time requirements for various OEsextracted from the LOPE”

“CHOOSE EXPERIMENT OBJECT (Rule-based procedure in the ExperimentChooser module)

-   -   “if single result produced from LOPE, then choose that OE    -   “if multiple possible OEs, then choose highest VOI; if equal VOI        then select at random    -   “if ZERO possible OE choices, then automatically loosen Chooser        constraints (e.g., loosen VOI constraints, time, safety) and        search again for a possible OE (Report to USER)    -   “if still ZERO possible OE, then return to USER, report and        stop.” “LOAD OE-Chosen”    -   (Based on the above criteria, because it has the highest VOI        score, the Experiment Chooser will load Exp #3).

“RUN EXPERIMENT OE-Chosen

Running the OE-Chosen activates other program modules in the ExperimentDirector module, which can include, inter alia, passing control to theExperiment Director Run Module that will direct the initiation of theexperiment based on the data in the EO-Chosen.

In at least one embodiment of the invention the Exp Director firstchecks to see it the EO-Chosen is a ‘self-running’ experiment type thatcan substantially direct its own initiation and progress (such as, forexample, a software program contained in the EO-Chosen software objectthat knows the location of its intended Experiment Chamber, contains allnecessary instructions for initiation and will itself direct theprogress of the experiment).

In other embodiments, the Experiment Director can gain information fromthe EO, then based on that information seek an Experiment Chamberappropriate for executing the EO from a number of Experiment Chamberproviders (such as available labs within one company, or from multipleCROs available at differing geographic locations), and then theExperiment director remains in control as to initiation and procedure ofthe experiment, taking from the EO-Chosen only static data as requiredby programs running in the Experiment Chamber.

“After each experiment step, evaluate progress and loop to nextexperiment stage”

-   -   (Here the Experiment Director may have interim progress-checking        steps in the procedure of the experiment, which may or may not        include accessing the DAE for interim evaluation).

Preferably, the Experiment Object that is chosen to run will have asmuch of the programmatic control of the experiment built in aspracticable (described as “process and chamber complete”). An EO that isprocess-complete and chamber-complete will only require the ExperimentDirector to pass to the EO the USG directives and other information fromthe domain ontology manuals that may be held by the Query Manager andthe Experiment Director modules. Preferably the EOs will have their owncapability to access their full domain knowledge bases directly.

In building efficiency into the ARS, it will be advantageous to minimizethe amount of information that must be stored within the ExperimentDirector module, allowing as much of the procedural information andexperiment-control routines as practicable to be maintained within theEOs themselves.

“Create Data and pass data to the Data Analysis Engine (DAE)”

-   -   In the current example, the EO is a virtual experiment that can        be implemented in an automated computer program that runs the        instructions of the experiment. It is a straightforward for a        software program to carry out the simulation of setting piece        positions in a 5×5 matrix and testing alignment of B versus W        pieces along rows, columns and diagonals, with each test        producing a data result that is reported to the data analysis        engine (DAE). Alternatively, the Experiment Director could send        the directions of the experiment to an Experiment Chamber in        which robots or human technicians manipulate the pieces on an        actual chess board and detect the presence or absence of        threats, reporting these results to the data analysis engine        (DAE).

In this example, running experiment #3, the domain guidelines (ChessManual) instruct a first positioning of first white queen on an edgesquare. With 16 different possibilities, there can be 16 iterations ofplacing the first piece and measuring a data result of the location andtotal number of non-threatened squares. The DAE can return through theExperiment Director an evaluation that every position yields coverage ofthe square upon which the piece sits plus 4 diagonal squares, 4 rowsquares and 4 column squares, for a total of 13 covered squares, alwaysyielding 12 non-threatened squares. Thus, the DAE program can choose anyone of these positions as being fairly equal; however, here the domainmanual guidelines may influence this choice by pointing to an edgesquare that cannot reach the center square in a single move as being aweaker placement, such that the Experiment Director can instruct theplacement of the next piece. Note however, that here the ExperimentObject can be using the domain manual guidance and it will beappreciated here that an EO that is a stronger software object itselfmay contain within its EO programs the capability to perform the interimpositional analysis and guided placement of the subsequent pieces, sothat the DAE and Experiment Director may be bypassed during theseexperiment steps).

“ANALYZE RESULT and test against GOAL

-   -   “Pass result/evaluation to Data Analysis Engine (DAE) and then        to Congruence Module to evaluate results progress against goal.

“If GOAL not reached, then ITERATE

“If GOAL reached, then STOP”

-   -   (In the case of the current example of “SS-1:Chess: 5×5        board-Queens-No-threat goal: Exp #3”, the result at each step is        evaluated for completion against the goal. The experiment        procedure calls for continuing until the three white queens are        placed, and the test results at this stage must show 5        non-threatened squares to provide a successful 100%        optimization).

At the end of the placement of the 3^(rd) white queen, the DAE passesthe outcome solution to the Congruence Module, which compares the USG tothe experiment result. If no gap exists between the result and the goal,then the ARS sends a completion report to the user and the programstops.

With multiple pathways possible in the experimental procedure of Exp #3,an unsuccessful result of one experimental cycle can lead to theCongruence Module passing the control back to the Experiment Directorwith an instruction to restart, whereupon the Experiment Director canadd as a constraint the rule to exclude any exact repetition of theprior experimental pathway. This ‘variation of parameters’ approach caninclude many appropriate methods from Monte Carlo research approaches aswell as from many approaches to finding mathematical solutions toproblems by iteratively varying parameters in certain equations andtesting for solutions.

Experiment Object Description

Each Experimental Object will have Value-of-Information properties,related to the set of experimental outcomes of that experiment, theprobability associated with each of those potential outcomes and anexpected value associated with each particular outcome.

Example 7. Experiment Objects—Type-1 Domain

In this Type-1 domain example, each experiment object is constructed fora particular Experiment Template, and supports the following operations:

-   -   Set parameter values. (Each Experiment Template defines a set of        parameters which distinguish one Experiment Object instance from        another.    -   Calculate Expected Outcome Set, given upper bound on number of        outcomes desired.    -   Execute experiment and produce an Experiment Outcome.        An Experiment Outcome has the following operations:    -   Update Knowledge Base with results of experiment        General Framework for Experiment-Related Object(s)

Experiment:

-   -   A System State Specification (such as an initial condition, or        starting state)    -   A System Modification Specification    -   Expected Outcome of experimental procedure

Experiment Outcome Object:

-   -   Experiment Result.    -   Progress Measure.

Expected Outcome Set:

-   -   List of Experiment Outcome Objects O_(i)    -   Estimated probability P(O_(i)) that each particular outcome will        occur.    -   VOI score V(O_(i)) associated with each particular        outcome=P(O_(i))×the Progress Measure of O_(i).    -   VOI (Exp)=SUM over all outcomes of Product of Probability of an        outcome occurring, Pr (Outcome i) and the VOI (outcome i)        Experiment-Related Object Framework for Chess Problem Example

Experiment Object Properties:

-   -   A Board State Object.    -   Specification of the move: (x₁, y₁)→(x₂, y₂).

Board State Object Properties:

-   -   List of board positions (x, y) for each queen.

Experiment Outcome Object Properties:

-   -   Board State Object represented.    -   Progress Measure: the number of unthreatened queens.

Expected Outcome Set Properties:

-   -   List of Experiment Outcome Objects O_(i), sorted by decreasing        total number of threats T(O_(i)).    -   VOI associated with each Experiment Outcome Object (this will be        set to the total number of threats T(O_(i)) for each Experiment        Outcome Object O_(i).

Knowledge Base Object:

-   -   Map from Board State Objects to number of unthreatened queens.    -   List of Board State Objects, sorted by decreasing number of        unthreatened queens.        Algorithm for calculating expected outcome set for a given        Experiment Object E:    -   1. Read upper bound on number of outcomes desired as N.    -   2. Create an empty list L of N/8 Board State Objects.    -   3. For all moves (x₁, y₁)→(x₂, y₂) which are valid for E's Board        State Object:    -   4. Apply the move to get resulting Board State B.    -   5. If B is not already in the Knowledge Base, then    -   6. Calculate T(B) as the number of threats in B.    -   7. If there is a Board State C in L such that T(B)<T(C), then:    -   8. Add B to L, replacing C if L is already full.    -   9. Loop back to step 3.    -   10. Create an empty list R of N Experiment Outcome Objects.    -   11. For all Board States B in L:    -   12. For all integers i from 0 up to 8:    -   13. Add an Experiment Outcome Object consisting of B as the        Board State and i as the number of unthreatened queens to R.    -   14. Loop back to step 12.    -   15. Loop back to step 11.

Example 8. Application Example for Business Method and ABRS Use in DrugScreening

An embodiment of the invention further provides for a user to interactwith a Query Manager module, as illustrated by the following‘pseudocode’ examples of partial scoping selections (where user responsechoices are indicated inside “quotation marks”:

-   -   Set Goal: “Select and prioritize lead compounds through an        efficacy screening assay”    -   Set Sub-Goal: “Use gene and protein expression profiles to        screen for efficacious compounds”    -   Set Domain “Biomedical—Drug Discovery and Development Pipeline”    -   Set Research Phase: “Late Discovery/Lead Prioritization”    -   Set KeyWords and Phrases: “Lead Selection, Lead Compound Screen”    -   Set Research Participants:        -   “Scientists involved in high-throughput screens (HTS)”        -   “Drug discovery scientists”        -   “ADME scientists”    -   Set EO Choice Parameters:        -   “Compounds from purchased combinatorial libraries”        -   “FTO eligible composition-of-matter”        -   “efficacy”        -   “patent rights”        -   “one organ system”        -   “liver”        -   “in vitro assay”    -   Set Budget parameters: “$ XXX dollars”    -   Set Deadline: “6 weeks”    -   Set Database: “Biomedical Ontology KB-01”    -   Set KB Integration “Genomics, Proteomics, Metabolomics,        Pharmacogenetics”    -   Set dimensionality: “6-D parameters; 10,000 limit each”    -   Set EO Type: “HTS assay”    -   Set ExpChamber Type: “Robotic”    -   Set DAE parameter: “[Autoselect]”

The above example of Query Manager settings chosen by the user (whichcan comprise the User-Specified Goal (USG) directive to the QueryManager and the ExpDir Modules) are meant to be illustrative only andare not meant to limit in any way the number, type, extent, form orformat of the range of potential user-interface interactions that couldoccur between the user and the Query Manager in various embodiments ofthe invention. For example, one preferred embodiment can provide furtherinteraction in the form of feedback from the Query Manager to the userthat extracts from experiment and/or research guidelines and/ortutorials that are stored in the system's knowledge base (KB) and/or onother distributed KBs within the research potential of the studiedsystem domain. Furthermore, the Query Manager in various embodiments canprovide functional interaction with the data residing in variousExperiment Objects as they rise toward selection by the ExperimentChooser component. For instance, information about potential assay CROsor collaborative laboratories could be returned to the user:

-   -   QM/ED RESPONSE: “Companies that have some assays in place:”        -   “Avalon (Taqman screen)”        -   “Pfizer/Pharmacia (P450 metabolism assay)”    -   QM/ED RESPONSE: “SNPs screening solutions off-the-shelf OTS:”        -   “Orchid”        -   “Luminex”        -   “Sequenom”            Such feedback to the user can be drawn from data resident            within the EOs within the LOPE (which can be distributed on            the network) or can be derived by a sophisticated version of            the Query Manager itself by accessing network information            based on parameter selection in the USG and information            developed from the EOs through the Experiment Chooser            component.

Example 9. ExpDir and Exp-CTRL Controlling ExpCH, Receiving Data and/orPassing Data to DAE

FIG. 11 illustrates a series of steps in how the Experiment Director ofan ARS such as provided by an embodiment can access a control accountfor an automated research laboratory, where the laboratory can offerhigh-throughput microarray experiment services and can employ theindustry standard ‘Minimum Information About a Microarray Experiment(MIAME)’ data/service interoperability protocol. Beginning at the top ofthe graphic and moving down by rows, the Experiment Controller cancreate an account with the automated laboratory, login, enter adescription of a pending or new experiment, enter descriptions forsample(1), sample(2) through sample(n) with treatment protocols for eachsample, declare the extraction protocols, which can be multiple for eachsample, declare labeling and hybridization protocols for severaldifferent hybridizations, which then flow into potentially numerousdifferent array designs, then each array can output data according to aspecific image analysis protocol, combining experimental data using atransformation protocol and finally submitting the data back to the ARSExperiment Controller and/or to the ARS data-analysis engine.

Example 10. Use of DAE with SLAM for QSAR Screening Analysis

Modeling the Descriptor/Activity Relationships in their Full Complexity

Typical QSAR applications use standard linear or near-linear correlationanalysis methods to predict activity from compound descriptors. Owing toreal biochemical/biological complexity, QSAR relationships can bereasonably expected to be nonlinear with respect to compounddescriptors. One ABRS method according to an embodiment of the inventionincludes a number of components to perform nonlinear modeling ofpredictive tasks. Combined with the identification of key descriptors,these nonlinear methods can provide a substantial improvement inscreening accuracy.

Greedy regression approaches are based on additive or linearrelationships between the individual predictors, i.e., relationshipsthat require the predictive descriptor sets be decomposed into separatepartial predictors. One ABRS according to a preferred embodiment of theinvention provides methods that are universally combinatorial, andtherefore do not require that the predictive sets be decomposed intoindividual components that are partial predictors separately. Such anABRS according to an embodiment of the invention can also deliver smallsets of descriptors that have the same or greater predictive power asmuch larger sets. Furthermore, focusing on a small set of combinatorialdescriptors facilitates rational chemical interpretation and enables thedownstream, more traditional QSAR computations to run faster and withbetter predictive performance.

Statistical Validation of Predicted Patterns

Valuable predictive patterns should be indicative ofchemical/biochemical/biological relationships, and should not be theresult of chance juxtapositions of values. The ABRS system includesnumerical approaches based on cross-validation and permutationcomputational studies on real data to measure the degree of chancegeneration of patterns as a means of providing statistical validation.

In addition to pattern recognition methods, one embodiment of theinvention provides a combination of sublinear association mining (SLAM)data mining methods (such as can be found and provided through theGeneLinker™ Platinum data analysis package, sold through ImprovedOutcomes Software, Kingston, Ontario, CA) with compute-intensivecross-validation for multivariate, multiclass Bayesian inference ofoutcome probabilities, such as is found in the Integrated BayesianInference System (IBIS™, available through Biosystemix, Ltd., Kingston,Ontario, Canada), allowing the DAE to distinguish chance occurrencesfrom predictive effects rooted in biology.

Additional data analysis methods and algorithms that can be incorporatedin the Data Analysis Engine according to one or more embodiments of theinvention include: LDA and QDA-based, univariate and multivariate PIA(Predictive Interaction Analysis—inferring interactions through outcomediscrimination and prediction), pair-wise gene-gene (variable-variable),combinations predictive of outcome, prioritized according tocomprehensive statistical scoring, CPIA (Competitive PredictiveInteraction Analysis), SPIA (Synergistic Predictive InteractionAnalysis); TEA (Theme Enhancement Analysis—linking data-supportedbiological functional themes to outcome discrimination and prediction),statistically-supported enhancements of informative gene groups; P12(Pathway Interaction Inference) through combined PIA and TEA, inferenceof competitive and synergistic pathway interactions, associations ofpathway interactions with clinical and biological outcomes; Gene NetworkReverse Engineering, cofluctuation analysis (associations across time,or condition, or assay, etc), continuous analysis, discrete analysis,linear and nonlinear analysis, multivariate analysis, cluster analysis,graph analysis, clique (identity cluster) extraction, multi-inputgraphs; ANOVA, F-test, multi-class tests, T-test, 2-class tests; MANOVA(multivariate ANOVA), 2-class tests, multi-class tests; Chip and classsimilarity analysis, Pearson correlation, Euclidean, other similaritymeasures as needed, Concordance, means of class-distances, distances ofclass-means; Discriminant Analysis, LDA (linear discriminant analysis),QDA (quadratic discriminant analysis), 2-class analysis, multi-classanalysis, univariate, multivariate, all of which can be found throughBiosystemix Ltd., Kingston, Ontario, Canada;http://www.biosystemix.com/pioneering %20applications %20and%20technology.html

Reverse Engineering methods can be included by a programmer skilled inthe relevant art utilizing the methods available above, as well asfollowing the methods in D'haeseleer et al. (See “First data-driven,reverse-engineered model of gene interaction networks derived frommeasured, high-fidelity gene expression data: D'haeseleer P., Wen X.,Fuhrman S., and Somogyi R., (1999) Linear Modeling of mRNA ExpressionLevels During CNS Development and Injury. Pacific Symposium onBiocomputing 4:41-52, the teachings of which are incorporated herein byreference in their entirety.

Example 11. Experimental Domains for Studied Systems—Biology DomainManual and Knowledge Base Content

A preferred embodiment of one Automated Research System according to theinvention provides for a Biological Annotation and Pathway ModelingLibrary having domain Knowledge Bases for at least a set of modelorganisms commonly used for research in biology, such as the followinglisted in Table 1.

TABLE 1 Model Organisms for which Annotation and Pathway ModelingKnowledge Bases are included in a Domain Manual Knowledge Base in apreferred embodiment and which can be part of the available experimentalresources of a participating automated laboratory. E. COLI Escherichiacoli common intestinal bacterium that can cause diarrhea disease S.CEREVISIAE Saccharomyces single-cell eukaryote, cerevisiae yeast knownfor role in bread and beer production S.S. POMBE Schizosaccharomycessingle-cell eukaryote, pombe yeast known for role in bread and beerproduction C. ELEGANS Caenorhabditis tiny, soil-dwelling elegans worm D.MELANOGASTER Drosophila ubiquitous fruit fly; melanogaster D. RERIODanio rerio zebra fish A. THALIANA Arabidopsis thaliana small weed thatmodels for the plant kingdom M. MUSCULUS Mus musculus house mouseIt will be appreciated that the above Table 1 is illustrative ratherthan limiting, such that much longer lists of potential experimentalorganisms could be part of the available resources for an automatedexperimental laboratory process and, similarly, extensive lists ofadditional strains, plasmid, compound, materials and/or otherbio-component libraries suitable for use in laboratories can beincluded.

Example 12. Method for Automated Design of HTP Experiments in Connectionwith a Computational Biology Learning System

In this example, a preferred embodiment of the invention furtherprovides for an automated laboratory (or an automated experimentalchamber, or a research robot), with certain experimental resourcelibraries accessible (such as model organism, strain, plasmid, compound,materials and/or other bio-component libraries), into which areconnected directives from automated experimental design componentsparsing Chosen Experimental Objects (CEOs) and from which automatedlaboratories' results are passed to data analysis, modeling andsimulation components.

In this preferred embodiment, the invention further provides for anintegrated experimental, modeling and management optimization system,with automated and goal-seeking feedbacks between experiment control,experimental results, modeling control, modeling results, query controland query results, connecting to libraries of available resources andconstrained by self-knowledge (the automated research system itselfknowing) of available resources.

The invention further provides for integrating scientific observations(or monitoring) with modeling and with artificial intelligence assistedmanagement and/or decision-making methodology. This methodology caninclude adjusting data-gathering in response to output from modelingmodules (modeling layer) and a manager's queries (e.g., the user's USGsubmitted through the Query Manager module).

The Integrated Management, Modeling and Measurement (IM3) methodologyaccording to an embodiment of the invention (a) translates into computerform the mental models of managers, (b) merges the formalized mentalmodels with prior and currently generated scientific models forexplaining relationships and dynamics in gathered and/or measured data,(c) makes the merged modeling layer transparent and accessible tomanagers and adjustably and robustly responsive to their queries, and(d) designs the data-gathering (automated experimental sampling and/orobservation) to be flexibly and rapidly adjustable to the data needs ofthe modeling layer as determined by the Congruence Module and ModelingModule in juxtaposition with the User-Specified Goal statement and theQuery manager and thus to the manager's queries as the manageranticipates a decision.

Example 13. AIM3 Research and Management in Area of Environmental Change

An Automated, Integrated Monitoring, Modeling and Management (AIM3)methodology according to the invention can be applied tointerdisciplinary study of and assessment of the potential impacts ofenvironmental change on society at varying scales in service todecision-making. As shown in FIG. 1, two dimensions of an integratedassessment in the context of climate change can be visualized. Onedimension can be thought of as a “vertical integration” that rises fromstudying causes and impacts through estimating risk and potentialresponses and then through decision-making to arrive at actualresponses. Another dimension can be illustrated here as a “horizontalintegration” of submodels for geospatial dimensions, resource sectorsand impact types that can be related to each level of the verticalintegration. This horizontal integration combines many physical andsocio-economic aspects of a regional case study that can be seen tointeract on a geographical scale. Regional, econometric input-outputmodels, for example can be built for each economic sector and theirinteractions with each other and with environmental changes mappedthrough a geographic information system (GIS).

Referring again to FIG. 1, an AIM3 assessment approach for examiningClimate Change Impacts according to an embodiment can include anintegration along one dimension, from observing causes and gatheringdata, through modeling risk and potential response analysis, to makingdecisions and actual responses, while an integration along anotherdimension (depicted as horizontal) combines research on physical andsocio-economic aspects as they compound and interact on variousgeographical scales.

Adjustable Data-Gathering Responsive to Modeling Layer and Manager'sQueries

As a manager anticipates a decision, he or she is motivated to gatheruseful information upon which to predicate that decision. Thisinformation can be made part of the manager's mental model or set ofmental models, a process that may be assisted by incorporating theinformation into computer models (which may be expert systems) to derivesecondary information that will guide and/or alter the manager's mentalmodel(s). It is useful to design the data-gathering process to beflexibly and rapidly adjustable to the data needs of the modeling layer.Further, the data-gathering step can be made adjustable in response to amanager's queries: the ARS environmental modeling module can include a3-dimensional, geodynamic, environmental modeling system in its modelinglayer, which can respond to a manager's query (for example, fromuser-specified goal (USG) directives), with the system able to recognizeits data needs relative to the USG objective function and to adjustablyinstruct the data-gathering process via the Query Manager and theExperiment Director in at least one embodiment.

Referring to FIG. 12, in a traditional method of decision-making,managers direct an information-gathering step 1204, which may includemonitoring or measuring, whereupon the information is returned to themanagers in step 1205. With the advent of numerical modeling, managementbegan to pass the information to a modeling group, as in step 1206, andthe results of the modeling would be returned to managers in step 1207.Integrating the monitoring, modeling and management methodology givesthe modeling division and/or the modeling objects various degrees ofcontrol over the monitoring process in step 1208, and brings the databack to the modeling process in step 1209. Then, automating the researchsystem with software objects that enable almost any user to have rapidaccess to a wide variety of experimental techniques and automatedlaboratories provides a further efficiency and acceleration to themethodology, such that automated, integrated monitoring, modeling andmanagement (AIM3) methods according to an embodiment of the invention,can provide a dramatically improved and powerful manner of conductingresearch on a wide variety of systems.

In an iterative learning model according to an embodiment of theinvention, an automated modeling-monitoring control linkage can beconnected to a rules-based, guidance module, such that thedata-gathering (which can be lab or field experimental, or ongoingmonitoring) can be automatically redirected by the guidance module basedupon the robustness of the result being created with the modelingroutine. This can be related to a Monte Carlo, iterative modelingexercise, where a series of parameter inputs are altered during a seriesof model runs to test sensitivity and robustness, except that in theAIM3 approach, instead of artificial inputs the model is receiving avariation of measured and/or gathered data. In fact, the two approachescan be used together effectively, where variation of parameters to yielda range of results can, through the rules-based guidance module thatevaluates value of information related to the expected value ofpotential experiment outcomes, determine the next-desired set of actualmeasurements to be obtained. In this fashion, the value of data gatheredis maximized and the modeling effort is able to focus more rapidly on aparticular response to a particular query.

Example 14. Environmental: AIM3 Research and Management in River Systems

FIG. 13 illustrates an Integrated Monitoring, Modeling and Management(IM3) methodology according to an embodiment of the invention, asapplied to water-resource management, which illustration follows inparallel fashion from the earlier description of an automated IM3assessment approach for examining climate change impacts in FIG. 1.Referring now to FIG. 13, according to an embodiment of the invention,an automated IM3 assessment approach for examining water-resourcemanagement issues can include an integration depicted along a ‘vertical’dimension that rises from monitoring (observing causes and gatheringdata), through modeling (including estimating risk and potentialresponses) in order to formulate guidance for decisions about responses,and can include along another dimension of integrated assessment(depicted as circles in a horizontal plane) combining research on manyphysical and socio-economic aspects of water-resource management thatcan be seen to interact on a geographical scale, including waterquality, municipal supply, human uses, flood control, land use, naturalhabitat protection, extreme weather and aesthetics.

Example 15. Environmental: Research and Management in Social EnergySystems

Global Environmental Change and Energy Resource Use

The invention provides further for applying an ARS together with IM3methodology for automated-IM3 (AIM3) learning in the domain of globalgovernance of energy resources, including assessing potential integratedassessment of global warming impacts, assessing global warming as asymptom of natural energy-technology feedbacks (ETF), building amodeling framework, and building an AIM3 global energy resource learningmodel.

The invention further provides for incorporating an analysis componenttermed “utilergy” with a specific definition, wherein ‘utility’ isdefined in terms of system service and is a scalable parameter formodeling and calculating “usefulness of energy”. The invention furtherprovides for defining one “util-erg” and a quantity, ‘utilergy’, asmathematical product of usefulness and energy.

Example 16. USG—Research Questions for an AIM3 Global Energy ResourceLearning Model

A user of an AIM3 learning model for studying global energy resource usecan set a user-specified goal (USG) directive to address the followinghypotheses and/or research questions:

-   -   (1) What are the changing patterns over time of energy flow        through various societal subsystems, both in terms of (a)        graphed node-arc subsystemic networks and (b) geographically        mapped storage, dynamic transport, through-flow and use?    -   (2) Are there patterns of growth, stability or a combination of        growth and stability that can be seen in an analysis of multiple        energetic subsystems within human society?    -   (3) How do patterns of growth, stability or a combination        thereof vary in causal networks and/or dynamic simulations when        analyzing the system in terms of a varying objective function        wherein each subsystem follows goal-directed rules to increase        useful energy density within the subsystem to a greater or        lesser degree?

Research Agenda (Relating to Research Objects in a Library of PossibleExperiments)

To achieve answers to the above research questions, an AIM3energy-resource learning model can choose Experiment Objects (EOs) thatmonitor energy ‘through-flow’ through various subsystems of the humansocial system and that analyze (and/or model) the impact of increases inenergy density in various subsystems. These approaches can include atleast the following EO categories, without limitation:

-   -   EO category 1—Tracking and mapping a set of parameters for each        subsystem, including reserves, extraction, transport, storage,        processing, consumption, conversion, price and growth, among        others (see FIG. 14, described below);    -   EO category 2—Deriving, through modeling, measures of “energy        intensity”, “energy density”, “useful energy” and/or “energy        usefulness” (or “utilergy”) in each subsystem and the extent of        feedback relationship between technology and energy through-flow        and/or useful energy in each subsystem (see FIG. 14);    -   EO category 3—Exploring multiple dynamic structures for an        average subsystem using constraint-based optimization, wherein        growth and stability are optimized within a set of constraints        and/or objective functions for each subsystem;    -   EO category 4—Modeling the management component of various        subsystems as a goal-directed function, where the goal is to        maximize growth, stability, and/or a combination of growth and        stability.

FIG. 14. illustrates an automated Integrated Monitoring, Modeling andManagement (AIM3) approach for studying human use of global energyresources, whereby utilergy and ETF are core variables among parameterssuch as reserves, extraction, transport, storage, processing,consumption, conversion, price and growth.

FIG. 15 illustrates a knowledge-base-assembly causal network for energyresource systems in an automated research system according to anembodiment of the invention, where “discover”, “extract (collect)”,“extract (drill)”, “transport”, “process”, “storage”, and “consume” areillustrative modeling parameters in the causal network, and where asub-network can be seen to be nested into multiple subsystems formed atdiffering levels of organization.

‘Utilergy’ as a Modeling Variable for AIM3 Studies of the Human EnergyResource System

The invention provides for improved definitions of “incorporatedenergy”, “usefulness”, “useful energy”, “usefulness of energy” and“useful energy density.” An embodiment provides for a novel modelingvariable, “util-erg”, which can be formed from a multiplication ofdimensional units of energy and redefined dimensional units of‘utility’, wherein ‘utilergy’ is characterized as having the dimensionof “usefulness of energy” in an energetic system.

Example 17. Modeling Framework: Growth Modeling Module for an EnergeticSystem

An ARS can provide for integrating a model of growth in any system as aninherent property of the energy within that system, as follows:

-   -   An Energetic Structure is an organizational process,        O.sub.r.sub.i, for which there exists an organizational radius,        r.sub.i. An energetic structure may be an energetic system.    -   An Energetic System is characterized by an organizational        radius, r.sub.(j=n), and is an assemblage of energetic        structures within an observed boundary, which structures are        characterized by organizational radii r.sub.(i<n).    -   The Observed Boundary is the minimum spatial boundary that will        circumscribe all the components of the energetic system, as        defined by inter-relationships between the energetic subsystems        comprising the system and by energy and material responsive to        those subsystems, as determined by an observer.    -   Dynamic Coordination is a process whereby kinetic energies        become stored through harmonization, or non-interference.    -   A Subsystem is a subset of energetic structures within an        energetic system that share a common functional relationship to        the system, which relationship differs from relationship of        other subsets to the system.        One embodiment of the invention further provides a “utilergy”        hypothesis that can be used in modeling experiments, the        hypothesis being that a positive feedback occurs in an energetic        system as increasing energy consumption amplifies techniques for        extracting useful energy from the environment (i.e., amplifies        the energy-technology feedback, or ETF). For instance, an amount        of energy (or a form of energy, or a particular flow-path        through the subsystem) that cannot increase the ETF can be        defined for the purposes of this embodiment as having little or        no usefulness, thus essentially zero utilergy. A form of energy        that can increase the ETF can be said to have a higher        usefulness, and consequently may have ‘x’ units of utilergy if        the ETF is enhanced by some ‘y’ percentage.

AIM3 Utilergy-Related Research

FIG. 16 illustrates a reduced-form modeling framework for describingrelations between environmental state 1601, energy-flow 1602, utilergy1603 and uses/benefits 1604, according to an embodiment of theinvention. Many relations will have a dependence on geographicallyreferenced energy-region attributes, such as topography, stratigraphy,land use or soil type, so that integrated modeling designed to interfacewith GIS tools will be advantageous. The modeling framework can betransferred and utilized by researchers in neighboring energy resourceregions, either directly or by adjustment from look-up tables based on amenu of regional characteristics commonly available. To build therelationships the research can be guided by field investigations and/orby previous studies of energy regions. FIG. 16 illustrates developing acombined factor of utility and energy (i.e., utilergy 1603) in a reducedform modeling exercise based on environmental state 1601, energy flow1602, and uses and benefits 1604.

Building an integrated model in a GIS-based framework that is able tocalculate and simulate the relationships shown in FIG. 16 can beimplemented by wrapping submodels (or component software objects) withan interface and coordinating the modeling routine with a controllerobject). FORTRAN, C, and Visual Basic modeling objects, for example, canbe controlled by C++ and/or Java routines. An embodiment provides forthe relationships to be described and assembled in a comprehensivematrix, or set of relational databases, as illustrated in the followingmodeling and/or analysis steps:

Modeling/Analysis Step 1605: Relations of Human Uses 1604 toEnvironmental State 1601.

Various uses that directly affect subsystem (urban, transport,infrastructure) condition, energy storage levels, infrastructure andsurfaces (transport) can be described in these relationships. Forinstance, building a settlement or a city may cause an energy-resourceregion to become reduced in some measure of quality. Or pumping oil mayreduce a reserve by some measure of usefulness. Environmental impacts ofeconomic decisions can be included. These relations are likely to varygeographically and can be specified as a function dependent on a GIStheme.

Modeling/Analysis Step 1606: Relation of Environmental State 1601 toHuman Uses and Benefit 1604

Ecosystem diversity and wildlife abundance of some measurable degreeleads to an environmental use at some measurable rate, which may varyfrom zero to some maximum rate. Energy-resource condition in a region,specified by utilergy 1603 (as related to energy “quality”) or energyabundance metrics for cities, energy and transport can lead toenvironmental use at some rate. Energy resource/reserve condition allowsa certain degree of human use. Benefits 1604 of these uses may bespecified by market and/or non-market valuations. Some of theserelations may be specified independent of geographical location

Modeling/Analysis Step 1607: Relation of Utilergy 1603 (Usefulness ofEnergy) to Environmental State 1601

Human habitat degradation or enhancement can be made a function ofutilergy 1603 at the entry point of energy flow into the subsystemand/or region of interest. These relations are specified per utilergyconstituent (e.g., subsystem growth, economic value, accessibility,etc.) and may be geographically specific. Relation of utility (asrelating to available energy quality) to local energy reserve condition(resource utilergy) may be described as a function of mining or pumping(exploitation) operations (geographically specified). Relationships ofutilergy 1603 to ecosystem species, health and abundance, and humanenvironment, may be based on observations and/or literaturedescriptions; e.g., energy flows causing high carbon emissions that leadto global warming and potential negative ETF consequences in somesubsystems can be docked with negative utilergy points. Feedbacks mayneed to be described here as environmental impacts degrade socialconditions, which in turn alters the ETF (and hence utilergy 1603)further. Some of these relations may be specified independent ofgeographical location, but others may depend on mappings of cities andother energetic subsystems.

Modeling/Analysis Step 1608: Relation of Environmental State 1601 toUtilergy 1603 (Usefulness of Energy)

Utilergy 1603 may be modified by retention or movement of energy througha subsystem or multiple subsystems by measurable degree, per constituentof energy flow 1602 and per residence time. Presence of humaninfrastructure may degrade (lower) utilergy 1603 by some degree perpopulation density if it impedes the ETF, whereas presence of othersubsystem processes may increase the usefulness of energy if theyenhance the ETF. Climate state, for instance, can be directly related tofossil fuel use, with fossil fuel use having a demonstrably positiveeffect on ETF in most subsystems. At a global level, many of theserelations do not need geographic specificity to be usefully studied in alearning model.

Modeling/Analysis Step 1609: Relation of Environmental State 1601 toEnergy Flow 1602

The condition of the resource region affects energy flow 1602.Stratigraphy and resource/reserve levels affect flow. Climate stateaffects flow through feedbacks that affect solar, wind and tidal energyproduction, as well as through weather events that affecttransportation. These relations are likely to be geographicallysensitive.

Further field observations are useful for calibrating parameters in themodels that encompass numerous interactions in the natural system thatare difficult to observe directly, either because we are ignorant oftheir mechanism or because they are too expensive to measure in detail.For instance, a single parameter for energy production in one aspect ofsupply may be derived, even though it is likely that field investigationin elaborate detail could discover differing rates of productiondepending on subtle characteristics within a single energy-productionregion.

To help explore and describe this relation, an AIM3 system can include aGIS-based energy production/supply model (i.e., production, transport,storage and losses), which can be linked to additional modules fromvarious “off-the-shelf” models. Examples of various such models can befound, such as models that develop linkages between ecological modelingand economic modeling in terms of equilibrium models, scaling andexternalities.

Modeling/Analysis Step 1610: Relation of Energy Flow 1602 toEnvironmental State 1601

Environmental state 1601 includes the condition of physical andbiological resources and standing cycles or patterns in those resources,including aspects of ecological stability and/or resiliency owing todiversity and multiple inter-relationships between species. Reducedenergy flow 1602 through a subsystem can impact the natural and humanenvironment in some describable measure. One of the chief concerns aboutfuture climate change, for instance, is how resilient is theenvironmental state 1601 to fluctuations in flow 1602 that couldaccompany fluctuations in raw energy supply and/or supply disturbance.Research in this area conducted through an AIM3 research systemaccording to an embodiment of the invention can include measuring,cataloging and describing these relationships.

Modeling/Analysis Step 1611: Relation of Energy Flow 1602 to Utilergy1603 (Usefulness of Energy)

This is a key relation to be derived from field observations in localenergy supply and use regions (or relevant subsystems), where possible,and from literature values where flow impact coupled to resource usecontribution can be extrapolated from other studies. These relations mayalso be model-derived, e.g., for those constituents of utilergy 1603that are related to rate-changes in subsystem characteristics onlydetectable through modeling. These relations are likely to be highlygeographically specific (e.g., doubling energy flow 1602 through aspecific urban subsystem can yield a different impact than doubling flowthrough a non-human subsystem. Examples include low-flow stagnationleading to loss of vitality in a region, or excessive overbuilding andactivity that can become counterproductive in terms of human health andsocial benefit.

Modeling/Analysis Step 1612: Relation of Energy Flow 1602 to Human Usesand Benefits 1604

Energy flow 1602 allows multiple uses to occur at some rate dependentupon amount or delivery rate, e.g., electrical production, industrialmanufacturing, mineral conversion and refining, up to some maximum peruse type. Some of these relations are geographically dependent, someindependent. These are direct relations, whereas indirect relationsthrough energy quality follow the functional path 1611 and 1614.Benefits are based on market and non-market valuations. A preliminarysurvey of energy use and users can serve as a starting point fordeveloping a comprehensive survey of energy resource users. Followingthis step, an energy allocation model can serve as a submodule tointegrate these relationships with other aspects of the integratedassessment model.

Observed physical impacts on energy usefulness (utilergy 1603, and/orenergy quality), on energy resource regions, cities and society will betranslated into economic impacts in an analysis that can build upon newobservation and historical data. Costs and benefits relating to energyresource use can be evaluated for relevant economic sectors and indexedto geographical location in the subsystem region of interest. Economicimpacts can be weighed against costs of differing strategies to protectenergy flow 1602 and reduce negative impacts at key sites, withconclusions drawn about which institutional strategies would bestprotect natural and human communities and the value of human uses. Theresearch must aggregate results at various scales, from very local toregional, utilizing GIS tools to contrast the environmental and economiceffects of centralized versus distributed institutional strategies.

Modeling/Analysis Step 1613: Relation of Human Uses 1604 to Energy Flow1602

Energy uses (withdrawals) impact energy flow 1602 directly throughdemand functions. Energy extraction, storage and transport regulationsaffect flow rates. Changing political control and exploitation patternsin a resource region can affect flow. Human energy use may alsoindirectly affect energy flow 1602 patterns through the complexmechanism of CO.sub.2 increase, global warming and consequentenvironmental changes (or events) that then impact energy flow rates(e.g., increased storm force and frequency affecting oil platforms inthe Gulf of Mexico). Some of these relations are location-dependent andsome too diffuse for specific regional modeling. Researchers can utilizean AIM3 system to explore to what degree information from specific,local studies can be extrapolated to anticipate broader impacts.Modeling/Analysis Step 1614: Relation of utilergy 1603 to human uses andbenefits 1604

This set of relationships, which are important to many of the potentialExperiment Objects (EOs) applicable to research in the domain of humanenergy use, comprise a matrix, with utilergy parameters 1603 as onedimension and a series of potential uses and benefits 1604 as anotherdimension. Lowering utilergy 1603 will limit the use of that energy flow1602 by some measure, to be determined by observation or byextrapolation from other studies (e.g., switching from high-grade oil tobiomass in some locations could increase cost of transportation andhence reduce use of transportation. Form of energy relates to its use.Again, benefit functions can be built on market and non-marketvaluations.

Modeling/Analysis Step 1615: Relation of Human Uses 1604 to Utilergy1603

Processing, conversion and transport functions can impact energyusefulness within a subsystem. Human-induced atmospheric cloudiness, forinstance, can reduce available solar energy in a region. Increasingwater use upstream can reduce hydroelectric production downstream.Distilling, concentrating and refining, on the other hand, can increaseenergy quality, making energy more useful for more and/or differentapplications. Increasing flexibility of use can allow innovation andmovement. Liquid fuels, for instance, are more portable and more easilyinjected into engines, and can have higher BTU/gram ratios and highercombustion rates, thus enabling airplane and jet transportation.

Modeling/Analysis Step 1616: Relation of Utilergy 1603 to Energy Flow1602

Liquid fuels can be transported more easily through pipelines.Electricity can be transported even more easily along wires suspendedabove the ground. The increase in energy usefulness represented byconversion of oil to electrical energy can be modeled by seeing itsrelation to increasing the ETF (e.g., by counting the reduced costs ofimplementing the transport of so many ergs from one location to another,or by counting the added benefits of having the more flexible,electrical energy source to build and maintain new energy-acquiringtechnologies, such as computers being useful for controlling nuclearreactions or enabling deep-sea drilling operations).

Developing an AIM3 Learning Model and the Process of Knowledge BaseAssembly

Building an automated learning interaction between data-gathering andthe modeling process can be characterized as growing a knowledge-baseassembly. This can be an iterative, growing process, where informationfed into the process can be more or less useful depending on the abilityof the results (or know-how) developed from that information (a) togenerate new, useful hypotheses and (b) to accelerate data-gathering.FIG. 17 illustrates research modeling components for an AIM3 energyresources learning model according to an embodiment of the invention.The knowledge-base-assembly engine 1701 can include data about theenergy resource network stored in a library database or knowledge base(KB) 1705. Associations among data parameters can be data-mined byassociation mining engine 1702 and reverse-engineering modelingcomponents 1703 (Bayesian classifiers and inverse modeling components)can interoperate with a simulation engine 1704 that is capable offorward simulation of system dynamics based on network parameters in thelibrary.

FIG. 17 further illustrates functional partitions of building a domainKnowledge-Base-Assembly 1701 for human energy resources and energy useplatform, wherein an association mining engine 1702 connects toreverse-engineering components 1703 and functions to create a causalnetwork model that can be based on AIM3 research experiments, while anEnergy Resource Causal Network Knowledge-Base (or Library) 1705 can beused to generate a causal network map that can be iteratively tested forcongruence with the experiment derived network mapping, and theconverged model can be tested using the Simulation Engine 1704. Greaterdetails are directly analogous to those shown in FIGS. 8A-8E above.

A knowledge-base-assembly cascade can be described that brings together(a) a reverse-engineered, energy-resource network model and (b) aliterature-based, energy resource system model/map (see FIG. 18,described below). The reverse-engineered model is derived solely fromdata, and is essentially a set of hypothetical models of varyinglikelihoods to explain the data. This data-derived model set is likelyto contain “unknown unknowns”, i.e., novel causative structures notpreviously discerned.

Referring to FIG. 18, in the context of research on human energyresources, a statistical analysis and association-mining step 1801identifies predictor sets for network modeling, which predictor sets canbe used at step 1802 to construct energy resource networks from timecourse information, identifying valuable (or strong) nodes in thenetwork, and detecting statistically exceptional inputs and outputs atsome nodes. At step 1803 a literature-based Energy System map can bedeveloped from the domain knowledge base, which can include interactivevisualization. At step 1804 a knowledge base assembly module comparesthe reverse-engineered and literature-based networks, testing forcongruence, and the process can be iterated to create an integratedmodel. At a step 1805, the system simulates perturbation effects usefulfor designing the next round of data gathering; VOI metrics can bedeveloped based on the simulation showing potential positive or negativegains in correspondence to known dynamics (based on random orprogressive variation of variables, which variables, if foundinfluential upon outcome and not currently mapped into the networkcausal dynamics with high certainty, can be made the subject of a nextexperimental goal (i.e., information gap to be closed) and thereforeexploratory experiments in a next round of experimentation.

For an AIM3 energy resources research system, an initial domainknowledge base can be developed from a literature-based mapping and canbe related to a set of models based on the existing collective wisdom ofprior research on energy metabolism in human society and dynamicmodeling of energy flow in the economy, topics which have been addressedin numerous studies (see for example: Worrell E, 1994. “Potentials forImproved Use of Industrial Energy and Materials.” Ph.D. Thesis:University of Utrecht; Wilting H C, 1996. “An energy perspective oneconomic activities.” Ph.D. Thesis: University of Groningen;Fischer-Kowalski M, 1998. Society's metabolism—the intellectual historyof materials flow analysis, part I, 1860-1970. Journal of IndustrialEcology, 2, (1), 61-78. Fischer-Kowalski M, and Hüttler W, 1998.Society's metabolism—the intellectual history of materials flowanalysis, part II, 1970-1998. Journal of Industrial Ecology, 2, (4),107-136; Battjes J. J., 1999. “Dynamic Modelling of Energy Stocks andFlows in the Economy: An Energy Accounting Approach.” Ph.D. Thesis:Center for Energy and Environmental Studies (IVEM), University ofGroningen; Haberl H., 2001a. The energetic metabolism of societies, partI: Accounting concepts. Journal of Industrial Ecology, 5 (1), 11-33;Worrell E, Ramesohl S, and Boyd G, 2004. Advances In Energy ForecastingModels Based On Engineering Economics. Annual Review of Environment andResources 29 (1) 345-381; Schenk, N.J., 2006. “Modelling energy systems:a methodological exploration of integrated resource management.” Ph.D.Thesis. University of Groningen, Groningen; de Vries H. J. M., vanVuuren D. P., den Elzen M. G. J., and Janssen M. A., 2001. ‘TheTimerIMage Energy Regional (TIMER) model’. Technical Documentation, No.461502024/2001, RIVM, Bilthoven; van Asseldonk M, 2004. “Modelling PowerExchange Between Norway And The Netherlands Through The Norned Cable.”M.Sc. Thesis: University of Twente/Norwegian University of Science andTechnology; Jensen, J. and B. Sorenson, 1984. Fundamentals of EnergyStorage. Wiley-Interscience, New York. Messner S. and SchrattenholzerL., 2000. MESSAGE-MACRO: linking an energy supply model with amacroeconomic module and solving it iteratively. Energy, 25 (3),267-282; McFarland J. R., Reilly J. M., and Herzog H. J., 2004.Representing energy technologies intop-down economic models usingbottom-up information. Energy Economics 26 (4) 685-707; all of theforegoing the teachings of which are hereby incorporated herein byreference in their entirety.

The collective wisdom may explicitly describe unknown areas andconnections, as well as characterizing uncertainties in these and otherareas; but, the literature-based models, and thus the knowledge-basesthat are assembled from them can be blind to the unknown unknowns in thesystem.

The knowledge-assembly module involves iterative fitting of the twoinput model sets, using congruence-testing and parameter variation. Manypossible causative relationships inferred in the reverse-engineeringwill fall away when merged with very certain known models, but in moreuncertain areas the reverse engineering will fill in gaps and enlargethe current view. A resultant best-fit model is then passed into asimulation module where perturbations to the system can be simulated totest effects on internal subsystem dynamics and dynamics betweensubsystems nested within an overarching system. The perturbation-testingcreates new hypotheses that direct another round of data-gathering.Referring to FIG. 18, a knowledge-base assembly cascade brings areverse-engineered energy-resource network model and a literature-basedenergy resource system model/map into the knowledge-base assembly model.

More details of the AIM3 Learning and Knowledge-Base-Assembly layer areshown in FIGS. 8A-8E (described in more detail above). Statisticalanalysis, association mining steps, and network reverse engineeringsteps are shown on the left (collectively 800) in FIG. 8A, while theliterature-based model assembly is described on the right (collectively801). The existing literature can be text-mined and parsed andauto-assembled into XML database structures. Ontologies allow sortingand sifting of the input text based on objects (nouns), interactions(verbs) and context, as well as resolution of ambiguous terms. Theacquired information is assembled into a set of energy flow-paths formultiple subsystems, where these pathways are structured into networkshaving nodes (objects/nouns) and arcs (interactions/verbs). Systems andsubsystems are formed at differing levels of organization, with the setsof nodes and arcs being mapped in the particular context of a particularlevel system. For example, a movement of oil may be mapped as shippingtransport from one port in one country to another port in anothercountry. At another level of organization, a movement of oil may bemapped as a piped transport from a corporation's underground tank to anelectrical generator. a reverse-engineered energy-resource network modeland a literature-based energy resource system model/map into theknowledge-assembly model.

Parameters for each subsystem can include energy reserves, extractionmodes, extraction rates, transport modes and rates, storage mode andvolumes, processing steps and rates, uses, consumption rates, conversionefficiencies, switching/conversion pathways, price and growth (in eachof many of the parameters), as well as other parameters. In bothmodeling approaches, deriving and mapping measures of “energyintensity”, “energy density”, “useful energy” and/or “usefulness ofenergy (utilergy)” for each subsystem and geographically is an importantand useful step. Deriving through the modeling the extent of feedbackrelationship between technology and energy through-flow and/or energyusefulness in each subsystem is another important step.

In the simulation module, dynamics and flux analysis can be tested toexplore robustness and noise sensitivity in the network model. Policyadjustment scenarios can be tested for effect on multiple parameters andparticularly the model-derived parameters, such as the ETF, utility andutilergy. Previous work on scenario formulation and models (GritsevskyiA, 1998. “The Scenario Generator: a tool for scenario formulation andmodel linkages.” International Institute for Applied System Analysis(IIASA), Laxenburg; hereby incorporated herein by reference in itsentirety) and energy policy models (Frei C. W., Haldi P. A., and SarlosG., 2003. Dynamic formulation of a top-down and bottom-up merging energypolicy model. Energy Policy, 31, 1017-1031; hereby incorporated hereinby reference in its entirety) can be compared with the updated data frommonitoring and subsequent simulations.

Query Manager Connecting Knowledge Base-Assembly and AutomatedExperimental Design

To automate the growth of an AIM3 knowledge-base assembly in oneembodiment, linkage is made to a Query Manager module that managesqueries (which can be programmed to include rule-based routines) andoptimizes the research progression by mapping particular classes ofqueries to experimental programs needed to gather data for the continuedmodeling and iterative fitting of the integrated energy use and resourcemodel. In the AIM3 methodology, this interface can be connected withvisualization for supervised learning in the hands of the modelers,and/or the interface can allow managers to access and modify the queryprocess directly.

A knowledge-base assembly engine according to one embodiment caninterface with a Congruence Module and pass further data needs(information gaps) to the Query Manager and a Research OptimizationInterface object in the Experiment Director to generate furtherexperimental design and data-gathering, and can further include aninformation management system (IMS) with a database component andautomated data-processing that can feed back into the knowledge-baseassembly functions.

A research optimization component built into the ExpDir module of an ARScan explicitly treat the question of value of information and usefulnessof a potential data-gathering step to the desired modeling goal and/orthe likelihood of gaining a robust answer to a query. As will bediscussed below, defining a “util-bit” can be applied in the context ofan information-knowledge feedback (IKF) process, where information-flowinto the knowledge assembly module can enhance and accelerate thegathering of more useful information, and where usefulness ofinformation can be defined as a function of the acceleration of theknowledge assembly. As the AIM3 system learns more about growth ofenergetic systems generally, the tight relationship of energy andinformation can guide the AIM3 system toward goal-directed rules thatoptimize information acquisition and knowledge assembly in a growingAIM3 Energy Use and Resource Model (which can directly provide regularand iterative growth to an Energy Use and Energy Resource domainKnowledge Base).

Parallel Between an AIM3 Learning/Research Model and Growth in EnergeticSystems

A preferred embodiment provides for a parallel function to exist betweenthe energy-acquisition process of an energetic system and theinformation-acquisition process of an AIM3 method utilized in automatedresearch. In modeling a living system, an energy-gathering step can bemade adjustable in response to a demand function and a dynamicorganization function (which may respond to the demand function) canrecognize its systems energy needs and adjustably instruct theenergy-gathering step. Here the demand function is parallel to amanagement function in the AIM3 structure according to an embodiment,while the dynamic organization function is parallel to the modeling(including the congruence testing and simulation) functions. As shown inTable 2, below, the three-component model can be generalized, a step Cis adjustable in response to a function A, while a function B, inresponse to the function A, can recognize its resource needs andadjustably instruct the step C. The generalization can align informationacquisition in an AIM3 model and energy acquisition in energy-resourcenetwork modeling. Management and Demand are related to end-user of theresource. Modeling and Dynamic organization are related to structuringof the resource into something that makes the raw resource more useful.Monitoring is related to acquiring information about the system, whichis parallel to an energetic system acquiring energy.

TABLE 2 Three component model generalized showing parallels between anautomated learning/research model (AIM3) and growth in an energeticsystem., aligning information acquisition (Monitoring) in AIM3 model andenergy acquisition in energy-resource causal network. General A B C AIM3method/ Management Modeling Monitoring system (structuring, (acquireusefulness) information) Energy Resource Demand Dynamic Acquire energyNetwork organizationOne embodiment of the invention provides for optimization and/orefficiency functions discerned and learned in the progress of AIM3-basedresearch on energetic systems (either from new experiments and/or froman Energy Use and Resource Knowledge Assembly (EUR-KA) to directlyinstruct optimization and efficiency functions in the AIM3 research andlearning method and system itself, with a preferred embodiment allowinga version of the AIM3 system to automatically generate new modulestructures and adopt such growth functions as the AIM3 system learnsfrom the EUR-KA.

Goal-creation objects, for example, can be programmed to include variouslearning goals, such as, e.g., a goal to reduce uncertainty in knownparameters; explore unknown unknowns (ascribe new parameters, forexample, as in creating and fitting an unknown data structure); solvespecific pathway in causal networks; complete causal network mappings,etc. It will be appreciated that numerous examples are available to oneskilled in the relevant art to generate and program goal-creationobjects.

The invention provides for goal-seeking routines to be built into one ormore of the Query Manager, the Experiment Chooser and the CongruenceModule, without limitation, where numerous parameter dimensions can becombined as n-dimensional ‘surfaces’ or vectors and the routine providesan optimization objective function to maximize this function in localdata space (i.e., to ‘climb’ the optimization surface). Methods toimplement such optimized goal-seeking through rules-engines arewell-known to one having ordinary skill in the art relating tooptimization.

It is instructive to consider analogous goal-setting components that areused in a simple positional component ‘virtual’ or ‘model’ system, suchas, for example, a chess program—where one primary objective functionfor a normal chess ‘experiment’ (or game) is to capture the opposingking, but the overall task involves numerous positional and tacticalsub-goals.

Example 18. Business Method—Illustrating Agreement Between a COMPANYImplementing the Invention According to this Example and a PHARMACUSTOMER for Implementation and Use of an Automated Biological ResearchSystem (ABRS) (and/or Automated Cure-Finding Method and System (ACFMAS))

According to a preferred embodiment, a COMPANY that has implemented anAutomated Research Service as a business method can engage in one ormore of the following steps:

a) Providing access to the ARS service for a fee to a customer, such as,for example, a pharmaceutical customer (‘PHARM-A’);

b) Enabling and allowing PHARM-A to operate the user interface and QueryManager of the ARS to create a User-Specified Goal (USG), includingaccessing the Experiment Director module (ExpDir) to choose anExperiment Object from among a set of EOs in a Library of PossibleExperiments (LOPE), which LOPE can be distributed over the Internetamong many companies and/or distributed LOPE databases. Preferably, theEOs share a common interoperability software object format, morepreferably the EOs and the ExpDir and the automated laboratory softwareobjects (many of which are described above) are based on object-orientedprogramming (OOP) design and further share the ANSI/ISA-S88 (Parts 1-3)International Batch Control standard (S88);

c) Enabling and allowing PHARM-A to execute the chosen Experiment Objectautomatically by so instructing the ARS;

d) Optionally brokering an automated Agreement based on a TemplateContract provided by the ARS to PHARM-A, which proposed contract can beof a format pre-approved by the automated laboratory CRO, or by COMPANYin the event that COMPANY is also the direct provider of the automatedlaboratory services;

e) Executing the contract with the parties, and PHARM-A directing the EOto be run;

f) Running the experiment and looping the ARS process as many iterationsas required to close the gap on the USG and then the ARS automaticallydelivering results to PHARM-A.

At step (e) above the COMPANY and PHARM-A preferably execute thecontract automatically, with the final brokered contract form and termsresulting from a rule-engine optimization based on business objectparameters set in the ARS by COMPANY (using business method softwareobject components of the ARS for information entry) and by the userPHARM-A (entering necessary information through the UI interaction withthe Query Engine and set into the User Specified Goal transaction).

A contractual agreement auto-generated from template forms within theautomated research system, according to one embodiment, can beillustrated by the following example and paragraphs:

“Research Service Agreement Company and PHARM-A

“PHARM-A Inc with an address at [STREET], [CITY], [STATE] [ZIP] and itsAffiliates (hereinafer “PHARMA”) and COMPANY, with an address at[STREET], [CITY], [STATE] [ZIP] (hereinafter “COMPANY”) enter into thisResearch Service Agreement (the “Agreement”).

“Whereas, PHARMA has generated preclinical and clinical experimentaldata in the area of inflammation, oncology, diabetes, MS and cysticfibrosis, regarding the pharmacological activities of PHARMA compounds;and

“Whereas, PHARMA has approached COMPANY and COMPANY has certain skillsand platform technologies to generate and experimentally developlearning pertaining to PHARMA's Experiment Query Information andExperiment Data and PHARMA Confidential Information (PCI) (as definedbelow); and

“Whereas, COMPANY will use, among other tools and databases, COMPANY'sAutomated Biomedical Research Technology (as defined below) to executePHARMA's chosen Experiment Object and subsequently evaluate PHARMA'sChosen Experiment Data together with PHARMA's Confidential Informationto perform Services (as defined below).

“Now, therefore the parties agree on the following:

“1. Definitions

-   -   1.1 “Affiliates” means with respect to a party, any corporation,        firm, partnership or other entity, which directly or indirectly        controls, is controlled by, or is under common control with such        party.    -   1.2 “Domain Specific Goal Solution” is a subset of the Domain        Knowledge Base (as defined below) and shall mean COMPANY and        Service-Specific Knowledge Base Assemblies that are comprised of        causal network statements in specific therapeutic or disease        areas, together with rule bases, the analysis and experimental        design components, and other automated reasoning technologies        specifically generated by the ABRS for PHARMA's use and designed        to act on PHARMA Experiment Query Information.    -   1.3 “Modeling Module” shall mean the portion of the ABRS        technology platform, including software tools, rule bases,        statistical computation, and know-how, that performs logical        reasoning over a domain represented in a causal network and        knowledge base to generate reasoned proposals predicting        possible causal correlations among multiple nodes and        interrelationships.    -   1.4 “Domain Knowledge Base” shall mean the structured        information in the ABRS databases and in distributed accessible        databases.    -   1.5 “ABRS Technology” means collectively the Domain Knowledge        Base (DKB), which includes Domain-Specific Goal Solutions,        Experiment Director Module, Data Analysis Engine, Congruence        Module, Knowledge Base Assembly and Modeling Module objects.    -   1.6 “ABRS Biomedical Model” means those causal statements        created solely by the ABRS in the course of conducting Services        which may incorporate information from publicly available        sources and does not include any PHARMA Confidential Information        and PHARMA Experiment Query Information.    -   1.7 “PHARMA Confidential Information” means all information on        therapeutic or disease areas, relevant literature not in the        public domain and all information about compounds as PHARMA may        disclose to COMPANY under this Agreement that is marked        Confidential, and if disclosed orally is reduced to writing and        marked Confidential within thirty (30) days of such disclosure.    -   1.8 “PHARMA Experimental Data” means experimental data generated        by PHARMA in both preclinical and clinical therapeutic areas,        submitted to COMPANY under a User-Specified Goal Service        Request. All PHARMA Experimental Data are also PHARMA        Confidential Information, whether or not marked as such, and are        exclusively owned and controlled by PHARMA with respect to        COMPANY.    -   1.9 “Service Specific ABRS Biomedical Model (ABRS-BM)” means        those causal network statements created by the ABRS in the        course of conducting Services, that are used to analyze PHARMA        Experimental Data and/or PHARMA Confidential Information and        that incorporate information from PHARMA Experimental Data and        or PHARMA Confidential Information. All assertions within        ABRS-BM shall be associated with their source attributions.    -   2.0 “Results” means all data, reports and deliverables,        hypotheses, and identified biomarkers generated by COMPANY under        this Agreement, as specified in each Service Request.    -   2.1 “Services” shall mean work by COMPANY employing ABRS        Technology pursuant to this Agreement. Services to be performed        are based on COMPANY'S written proposals (each a “Proposal”) in        response to electronically submitted User-Specified Goal        directives comprising one or more research service requests for        a PHARMA project from PHARMA (each a “Service Request”) as        provided below.    -   2.2 “Term” means twelve (12) months from the date of execution        of this Agreement by COMPANY or completion of the Services,        whichever is earlier, or unless earlier terminated pursuant to        this Agreement.” [etc., followed by other contract terms].

FIG. 19 illustrated series of Business Method steps according to anembodiment, and illustrates semi-supervised business steps according toa further embodiment, wherein: at step 1901 a customer places an order;at step 1902 the order information is received and/or registered and/orrecorded; at step 1903 the order information is matched against adatabase listing 1904; at step 1905 a service order is generated, whichcan be an automated step; at step 1906 a service order memo is createdin email and or printed (in which fields are automatically filled infrom the order information and/or the database information extracted incorrespondence to the order information. The memo can read as follows,or in similar fashion:

“Dear <<AUTOMATIC LAB SERVICE PROVIDER>> Please carry out standardprocedure <<EO I>> for customer <<XYZ>>. Attached are EO procedureprotocols, Agreement terms and payment details. Sincerely, <<SSPCompany>>”

At step 1907 the Experimental procedure is specified from a rule-basedsoftware engine that links the customer information and desired locationfor the procedure to appropriate and available protocols that are storedand indexed in the ARS Company database (which can include specificprotocols for the Experiment Object, a mailer specification and labelare generated, and payment to the automated lab service provider isdetailed and scheduled; and step 1908 is transmission of the completedservice order to the lab. Steps 1906, 1907 and 1908 can be automated.

A further embodiment provides another example, illustrated by referenceto FIG. 20, of a sequence of business steps according to the invention.FIG. 20 generally shows a flow chart describing a Web-based orderingprocess that is connected to automated generation of service-order thatdirect the steps of performing automated experiment service steps,reporting and delivering data results to a customer; and/or deliveringto the customer digital keys to access the knowledge base, and/orcode-release keys to initiate electronic delivery of the stored dataresults from a server. In more detail, the following steps areillustrated in FIG. 20: At step 2001, a customer accesses a web sitethat offers the automated experiment service product. At step 2002, thecustomer orders services on the web site, such as, for example,providing experiment domain and user-specified goal information,providing dates, choosing level of service, entering data or meta-datato be included later in (or on) the results. This information caninclude names, dates, prior experiment and/or data history, inter alia.as well as information that may be subsequently and automatically pulledfrom other 3^(rd) party databases through the Internet in response toinformation entered by the customer. At step 2003, the customer pre-paysfor automated services (such as, for example, paying by credit card, orpaying by pass-through billing to an automated laboratory and/or throughcollaboration fees). At step 2004, the customer agrees to a bindingcontract (including, without limitation, a legal electronic signature, awaiver concerning liability, and/or the customer expressly assuming riskand liability on behalf of the experiment, or the risk is partitioned).

At step 2005, the ARS company server connects the customer's orderinformation to a ARS company database or to one or more 3^(rd) partydatabases to obtain additional data or information to be used ingenerating a service order and/or used in subsequent formation anddelivery of the service, such as, without limitation, information aboutprocedures, locations, practitioners, laboratories, regulations,materials, costs, risks, probabilities, service delivery, postaldelivery, scientific data, data analysis and other information relatedto the customer-provided information and/or related to informationneeded for the service order. Typically, this data will be pulled fromthe ARS company and 3^(rd) party databases by software program routinesthat automatically generate the service order; some of this informationcan be independent of the information provided by the customer's orderentry, while other information can be dependent upon the customer'sorder entries. At step 2006, the company software automaticallygenerates a service order to participating laboratories and/or otherservice providers. At step 2007, the ARS company software transmitselectronically the Service Orders and portion of prepayment to thelaboratory (optionally including a preaddressed postal or courier mailerenvelope that can be subsequently used by the laboratory professional(s)to send the results directly to the customer if requested). At step2006, the laboratory services are performed by the professionalpractitioners in the automated experiment chamber (for example, withoutlimitation the Experiment Object is parsed by the ExpDir module and theExp Controller directs the initiation of the experiment at thelaboratory, samples delivered labeled with data and/or code trackinginformation if applicable). At step 2009, data is entered by laboratoryprofessionals, converted and/or transferred automatically onto the datacomponent that is to be stored with the results (such as, for example,information about customer, experiment, the experiment sequence and/orprotocol, the sampling, data processing and analysis procedures. At step2010 the data component is merged with the results onto the ARS server,such as by an automated electronic transmission procedure. At step 2011,the results report is packaged in a transmission, which can be anelectronic report that has been preaddressed to the customer or to acentralized server facility (such as described at step 2007 above) orwhich can be an electronic, data-structure packaging for electronictransmission directly to a data processing module and/or DAE module. Atstep 2012, the transmission is sent/delivered to the customer, or atstep 2013, the package can be sent to a centralized domain knowledgebase server or to a 3^(rd) party, such as a collaborator handling thenext stage of the R&D. At step 2014, a code or key can be sent to thecustomer allowing later recovery from the server facility or knowledgebase (where the code can be a digital password that allows the customerto signal a server to automatically transmit the stored data to thecustomer or to another 3^(rd) party).

FIG. 21 illustrates a succession of web pages or web screens that canappear according to an embodiment as part of the business method ofproviding an offer to a potential customer and the recording of orderinformation and completion of the ordering transaction, wherein: a firstbusiness offering web page 2101 can present to the customer a choice ofobtaining a description of services and/or a hyperlink to begin anorder; a subsequent web page 2102 can include data-entry (or text-entry)windows 2104, which can include pull-down data selection windows (ormenus of participating professionals, automated laboratory services),for the customer to enter identifying and transaction information, suchas but not limited to customer name and address, experiment desired (orestimated sampling procedure required); a further secondary screen 2103containing level of service choices 2106 (e.g., an inexpensive customeroption can include simplest lab method, with results simply delivered tocustomer, whereas a more expensive option may include an expensive dataanalysis method, modeling and simulation analysis, etc., being moreelaborate and/or complete, then a further secondary screen 2105providing cost, invoice and/or payment information (for example, withoutlimitation, an initial order fee, a experiment preparation fee, aresults report and delivery, shipping fees if applicable, if the ARSCompany is providing licensed subscription, an annual subscription fee);a further web-page screen 2107 providing legal terms, which screen caninclude an interactive button to register customer's agreement to thelegal terms (such as, without limitation, providing RegulatoryDocuments); and, inter alia. a further page 2106 that can include aninteractive button (“ORDER”) to cause the order to be generated, and/orto initiate the processing of the submitted order, i.e. initiating theautomated research.

Referring to FIG. 22, according to an embodiment of the invention,automated research system services can be used as an aspect of abusiness method for multi-party collaboration 2202, wherein successivestages 2203 of R&D 2200 can be created by multiple parties 2201interacting to promote the automated research progression.

Architecture: Modularizing Control Code in EOs

In another aspect of the subject invention, a standards-based model canbe employed to modularize the control code into easily testable blocks.By applying and using modules as the building blocks for eachExperimental Object application, the creator of the EOs are able to testeach of the components, one at a time. This provides a systematictesting protocol. As the solutions presented by the EOs incorporatingsub-EO techniques grow, the higher order modules are built upon testedand approved modules. The testing of the higher order modules can belimited to the new code in the higher order modules.

In yet another aspect thereof, a rules-base engine is utilized whichaccommodates decision-making for research laboratory processes, as wellas steady-state and long-term projections for high-level researchprocess decisions (e.g., minutes, hours, and days between decisions).This decision-making capability enables the equipment and/or researchtools to achieve faster performance (throughput) for multiple,distributed end users. The rules engine provides an environment forsophisticated programming of continuous and discontinuous expert-baseddecision making procedures with regard to lab research processes in amanner understandable to non-process experts, non-batch experts andnon-control experts. This is applicable also to ease of use, systemmaintainability, repeatability, testability, reduction of complexity,programming/development efficiency.

Preferred embodiments of the subject invention achieve improvedlearning, research process and laboratory equipment utilization andenhanced experimental results in the conduct of investigating one ormore studied systems (such as, e.g., environmental or biologicalsystems, by implementing a smart rules engine in conjunction withExperiment Objects that can automatically direct laboratory experiments.The experiment objects (EOs), in a preferred embodiment, can bedeveloped by many different groups, persons or companies having ordinaryskill in the art, using the ISA S88.01 International Batch ControlStandard (hereinafter “S88”). A rules engine can be employed with thestandards-based control code in order to optimize the flow ofexperimental control through an individual piece of equipment orgroup(s) of equipment (such as a research robot and/or an automatedlaboratory). The S88 methodology provides opportunities for modularityand standardization which is strongly compatible with an object orienteddesign. Standard instrumentation protocols, and equipmentconfigurations, for example, can be grouped into Equipment Modules (EM)classes and control module classes. The EM is a grouping of controlmodules that represent process functionality, wherein the controlmodules are equipment used in the process. A symbol is provided for eachstate of the EM. The control module provides a symbol for each operatorinterface (e.g., auto-manual switches), a symbol for each control systeminput and output, and a definition of the control logic of the controlmodule. Each instance of a module class is easily linked to unique fielddevices and equipment using aliases.

An embodiment of the invention provides for utilizing the S88standards-based model to modularize experimental control code intoeasily maintainable modules. Each module can have a standardcommunication protocol to interact with another module. Functionalitywithin the module is documented and isolated from other modules. Byseparating and isolating the modules, when a change or a problem occursit is easier to isolate the module that corresponds to the requiredfunctional module. The overall solution is assembled from the modularstructure. The solution can be controlled and monitored by a commercial,S88-based software package.

This standardization can start with automation software within theresearch equipment in an automated lab and/or can extend to automationdirective software in the EOs and in an experiment control module of theautomated research system, creating a lower cost, more reliable solutionand ends with an interconnected, information-enabled researchenvironment that can utilize process data to optimize the process withina piece of equipment or across multiple pieces of equipment.

The rules-base engine accommodates decision making for high-speedprocesses (which in one preferred embodiment of the invention can be onthe order of about 10 msec per decision), as well as steady-state andlong-term projections for high-level process decisions (e.g., minutes,hours, and days between decisions). This high-speed decision-makingcapability enables the equipment to achieve faster performance(throughput) for end users.

An embodiment of the invention increases functionality by applying theS88 architecture at the controller level along with S88-basedsupervisory software. All equipment functionality can be broken downinto elementary control and equipment modules. Supervisory executionsoftware can then used to link these equipment modules intodeterministic sequences to support the overall experimental procedurespecifications. This separation of equipment control and supervisoryexecution supports the capability to create any allowable sequencing ofevents across the equipment, thereby increasing the overallfunctionality of the integrated, distributed laboratory. Rather thanbeing constrained by conventional “hardwired” sequencing controlstrategies, the developer and end user now have the flexibility toprovide any required sequence of events across the research laboratoryor many laboratories.

The rules engine provides an environment for sophisticated programmingof continuous and discontinuous expert-based decision-making proceduresin a manner understandable to non-process experts, non-batch experts andnon-control experts. This is applicable also to ease of use, systemmaintainability, repeatability, testability, reduction of complexity,programming/development efficiency.

This innovation significantly reduces the cost of developing newequipment, maintaining existing equipment, trouble shooting fieldproblems, and retrofitting/updating existing equipment.

Although the description focuses on the S88 architecture, other similarmodularization architectures (e.g., object-oriented designs) can beemployed in combination with the rules-based engine in order to achievethe benefits described herein with respect to the S88 architecture.

Referring now to FIG. 23, there is illustrated a methodology of objectoriented and rules-based lab research process monitor and control inaccordance with the invention. While, for purposes of simplicity ofexplanation, the one or more methodologies shown herein, e.g., in theform of a flow chart, are shown and described as a series of acts, it isto be understood and appreciated that the subject invention is notlimited by the order of acts, as some acts may, in accordance with theinvention, occur in a different order and/or concurrently with otheracts from that shown and described herein. For example, those skilled inthe art will understand and appreciate that a methodology couldalternatively be represented as a series of interrelated states orevents, such as in a state diagram. Moreover, not all illustrated actsmay be required to implement a methodology in accordance with theinvention.

FIG. 23 illustrates a methodology of object-oriented and rules-basedprocess monitor and control in accordance with the invention, where theExperiment Director can control the automated laboratory process at step2300 as the program receives the process, at step 2301 the S88 modelmodularizes code module(s), at step 2302 invoke the communicationsprotocol, at 2303 load the code modules, at 2304 use the rule-engine tomake research decisions and at 2305 make process adjustments. Inslightly more detail, at 2300, a research process (such as a biomedicalresearch process) is received for control and data acquisition. At 2301,a standards-based model (e.g., S88) is employed that is based on controlcode modularized into libraries of code modules. At 2302, acommunications protocol is provided for inter-module communications,which protocol standardizes communications between most, if not all, ofthe code modules. At 2303, one or more code modules are loaded intocompatible automated experimental process devices for execution tocontrol one or more pieces of automated research equipment. At 2304, therules engine is employed in communication with devices and/or codemodules such that rules which are written can be imposed by executionvia the rules-engine in the Experiment Director Module to makeintelligent decisions in real-time associated with, for example,corrections and adjustments of research process conditions, as indicatedat 2305. This facilitates optimization of at least process flow, deviceuse, and experimental result throughput.

FIG. 24 illustrates a system 2400 of devices that can be employed andconfigured for process control in accordance with the invention.Depicted is a plurality of the devices 2403 (denoted as DEVICE.sub.1,DEVICE.sub.2, . . . , DEVICE.sub.M) that can be utilized to instrumentone or more processes and associated equipment (denoted collectively as2407). Each of the devices 2403 can be used for a different purpose. Forexample, a first device 2404 can be used to control a robot arm, and asecond device 2405 can be configured to monitor and control a processchamber, such as an incubator. Accordingly, the first device 2404 willbe loaded with one or more code modules 2408 (denoted as MODULE.sub.1, .. . , MODULE.sub.N) that perform dedicated functions for which the firstdevice is assigned. Similarly, the second device 2405 can be loaded withone or more code modules 2401 (denoted MODULE.sub.1, . . . ,MODULE.sub.X) that form the modularized code needed for operation andfunctioning of the second device to monitor and control the processchamber of the equipment/process 2407. Furthermore, the system 2400 caninclude an Mth device 2406 utilized for data acquisition of varioussensor measurements associated with the equipment and/or process 2407.Accordingly, the device 2406 includes one or more code modules 2402(denoted as MODULE.sub.1, . . . , MODULE.sub.Y) which are uploadedthereinto for acquiring data and operation of the device 2406.

The modules 2408 of the first device 2404 intercommunicate with eachother via the standardized communications protocol. Similarly, modules2401 of the second device 2405 intercommunicate with each other via thestandardized communications protocol, and modules 2402 of the Mth device2406 communicate with each other via the standardized communicationsprotocol. It is further to be appreciated that since the code modules(2408, 2401, and 2402) intercommunicate with the standardized protocol,inter-module communications can also occur inter-device. In other words,the first module (denoted MODULE.sub.1) of the first device 2404 cancommunicate across a communications network (or bus) to a first module(denoted MODULE.sub.1) of the second device 2405. Moreover, some of themodules employed in the devices (2404, 2405, and 2406) can be the same.For example, the first modules (denoted MODULE.sub.1) of each device(2404, 2405, or 2406) can be code that performs basic setup andconfiguration of the device, where the devices are the same model, etc.Yet other code modules loaded thereinto facilitate operation andfunctionality for different purposes related to the equipment and/orpart of the process to be instrumented.

FIG. 25 illustrates a method of device preparation and operation for aprocess in accordance with the invention. At step 2501, the EO processto be performed is determined. At step 2502, one or more tools in anExperiment Chamber are assigned to the process. At step 2503, a deviceis assigned to the process and/or process equipment for toll controland/or data acquisition. At step 2504, modules compatible with theselected device are selected and uploaded to the device. At step 2505,one or more of the uploaded code modules are tested in the device. Atstep 2506, the device can then be installed in the system. Note that itis to be appreciated that the device can already be installed in thesystem such that a tool replacement is required and not the deviceitself. The process is then started, as indicated at step 2507. At step2508, rules are imposed and executed before, during, and/or after theexperimental process runs to make adjustments and/or corrections tooptimize system processes, for example. At step 2509, device softwaremodules parameters can be adjusted in real-time according to the rulesto account for process changes and/or equipment wear and failure.

A preferred embodiment provides for a methodology of implementingparallel devices for a critical process in accordance with theinvention. The critical process is determined and two or more devicescan be selected and assigned to the process. Note that the two or moredevices can be the same or different, which is not a limiting factor,since the code modules are optimized for the given device model.Supervisory control exists for any device type, since inter-modulecommunications is according to a standard protocol. At step 2508, Rulescan be imposed and executed to determine device integrity and health ofthe device and associated tools and process being controlled and/ormonitored. If a change is not detected in a first device, parameter,tool or the process, flow is returned to continue rules execution fordetermining if changes have occurred. If changes have occurred, flow ispassed to second or third devices, and can even move the affected firstdevice offline, leaving the second device online to handle theprocessing required. While offline, a diagnostics module of the changeddevice can be executed (step 2510) to determine a cause of the change.

Artificial Intelligence Components to Automate Features of AutomatedResearch

In still another aspect of the invention, an artificial intelligencecomponent is provided that employs a probabilistic and/orstatistically-based analysis to prognose or infer an action that a userdesires to be automatically performed.

Programming

An ARS according to one embodiment of the invention can be programmed byone of ordinary skill in the art using a number of build environments(such as, for example, MS Visual Studio; MS InterDev; C++; Java;RDF/XML/OWL.

In a preferred embodiment of the invention, object-oriented programming(OOP) approaches are used to build software objects, such as, forexample, without limitation as to numbers of object categories or numberof objects per category:

1. Automated Laboratory Objects

-   -   Laboratory Control Objects    -   Robot and Automated Instrument Driver Objects    -   Sample and Materials-Handling Objects    -   Annotation Tracking Objects    -   Laboratory Information Management System Objects    -   Variable/data acquisition and recording Objects    -   Laboratory Resources Objects    -   Laboratory Contract Services Objects    -   Laboratory Technical Objects    -   Laboratory Experiment Controller Objects    -   Environmental sampling control objects    -   Laboratory QMS documentation management objects    -   Laboratory Safety Objects

2. Experimental Director Objects

-   -   Experiment Design Objects    -   Parameter List Objects    -   Parameter Uncertainty Objects    -   Constraint-Modeling Objects    -   Hypothesis-Formation Objects    -   Value-of-Information Objects    -   Goal Objects        -   Goal Seeking Objects        -   Goal Creation Object    -   Experiment Chooser Objects    -   Experiment Chooser Rule-Engine Object    -   Experiment Director Experiment Controller Objects    -   Quality Assurance/Quality Control Objects    -   QMS documentation management objects    -   Experiment Safety objects

3. Data Analysis Objects

-   -   Data Analysis Rule-Engine Objects    -   Data Processing Objects        -   Image processing objects        -   Data Annotation Tracking Objects        -   Data normalization Objects        -   Data Tabulation and Graphing Objects    -   Statistical Analysis objects (OTS-Spotfire; GeneLinkerPlatinum)    -   Association Mining and Reverse Engineering Objects (GL-P)    -   Math Solver Objects/Algorithm Objects [numerous]

4. Dynamic Modeling and Simulation Objects

-   -   I/O exchange/transaction objects    -   Structural & Network Graph Differentiation Objects    -   Self-organization-level Objects    -   Hierarchical Nesting Objects    -   Node/Component Interaction Objects    -   Nested dynamics sequencing objects    -   Dynamic modeling parameter objects    -   Simulation Run Objects    -   Positive Feedback Modeling Object (system invokes when certain        conditions met)    -   Negative Feedback Modeling Object

5. Knowledge Base Assembly Objects

-   -   Knowledge Base access and update objects    -   Congruence testing objects    -   Fitness measure objects    -   Ontology objects    -   Pathway objects    -   Bayesian Inference Objects    -   Causal Networks objects    -   Signaling objects

6. Query Manager Objects

-   -   Query formulation and SQL objects    -   Network access objects    -   Knowledge base parsing objects    -   User-interactive I/O objects    -   User-Specified Goal definition objects    -   User Business Broker objects

7. User Interface Objects

-   -   GUI and visualization objects    -   User customization objects

8. Database and DB Management Objects

-   -   Information Library Objects (interact w/Ontology Objects)        -   Domain Ontologies and Semantic Web objects    -   Library of Possible Experiment Objects        -   Experiment Objects            -   Experimental Technique Objects            -   Experimental Equipment Menu Objects            -   Experimental Procedure Objects            -   Experimental Outcomes Objects            -   Experimental Materials Objects            -   Experimental Equipment Control Objects            -   Experimental Sequencing/Scheduling Objects            -   Experimental Costing Objects            -   Experimental Sourcing/Siting Objects            -   Experimental Technical Objects            -   Experimental Variable/Data Objects            -   Experimental Contract Services Objects            -   Experimental QMS/Regulatory Objects            -   Experimental Safety Objects            -   Experimental IP Ownership Objects

9. Business Objects

-   -   Business Method Management objects        -   ARS owner hosting/subscriber objects        -   Transaction templates objects        -   Contract Brokering objects        -   Contact Management support objects    -   RFP/Proposal management objects    -   Market analysis objects    -   Price modeling and quantity adjustment objects    -   Legal objects        -   Template terms objects        -   Royalties terms and calculations objects        -   IP ownership and FTO analysis objects        -   Licensing and contract terms and adjustments objects        -   Warranty and indemnification objects        -   Arbitration terms and management objects        -   Regulatory and QMS certification objects    -   Budgeting analysis and assistance objects    -   Risk analysis objects    -   Web 2.0 Social Networking Interface Objects

It will be appreciated that the ARS described herein in certainembodiments, including pseudo-code illustrating the methods and systemof embodiments of the invention, can be implemented by one skilled inthe art of software programming in one or more different programminglanguages, or combinations of programming languages, including, forexample, such languages and programming tools and approaches asobject-oriented programming (or OOP, including, without limitation,software objects, software classes, databases, loops, relationaloperators, pointers, inheritance, polymorphism), C#(including C# version3.0), JavaScript, Python, C++, C, Perl, Visual Basic, PHP, AsynchronousJavascript and XML (AJAX), the .NET Framework 3.5, ASP.NET 3.5 andASP.NET AJAX, Database/SQL/LINQ, XML/LINQ, WCF Web Services, OOD/UML,XAML, Visual Studio 2008, SQL Server Express, Transaction-StructuredQuery Language (T-SQL), HTML, XHTML, DOM API, XSLT and XPATH, CSS, XML,SVG, HTTP, SQL, XForms, WS-* Services and SOAP, CORBA, DAML+OIL, RDF,OWL, Web 2.0, WSDL, WS-* Services and WSDL, JSON, Java Servlets, securesocket layers (SSL), Mashups, RSS, Atom Syndication Format (ASF),AtomPub, web-based ontologies, and further using, among other known anddescribed programming methods and approaches, the programming methods,routines, techniques and technologies known to practitioners anddescribed in the following treatises, which are each incorporated hereinin their entirety: “Ajax Bible.” Steve Holzner. Wiley Publishing, Inc.,2007, Indianapolis, Ind. 695 pp.; “C#2008 for Programmers. Third Edition(Deitel Developer Series). Paul J. Deitel and Harvey M. Deitel. PrenticeHall, New York N.Y., 2008. 1251 pp.; “Programming Python.” Mark Lutz,O'Reilly Media, Inc., Sepastapol, Calif. 2006. 1552 pp.; “Pro T-SQL 2008Programmer's Guide, “Michael Coles, Apress, Berkely Calif. (2008), 659pp.; “Professional Web 2.0 Programming,” Eric van der Vlist, DannyAyers, Erik Bruchez, Joe Fawcett, Alessandro Vemet, 2007, WileyPublishing, Indianapolis, Ind. 522 pp.; “Beginning C#3.0: Anintroduction to Object-Oriented Programming,” Jack Purdum, 2007, (Wrox)Wiley Publishing, Inc., Indianapolis, Ind. 523 pp.

The Data Analysis Engine (DAE) module according to one embodiment can beprogrammed readily by one skilled having ordinary skill the artfollowing methods outlined in “Introduction to Combinatorial Analysis,John Riordan, Dover Publications, Mineola, N.Y., (2002), herebyincorporated herein by reference in its entirety, and can include anyone or more of methods for combinatorial analysis, including withoutlimitation, permutations, partitions, compositions, trees, networks,functions, inclusion and exclusion.

One embodiment of the ARS according to the invention provides for anautomated research system prediction of next-round (or next loop)experimental results to be able to satisfy the new constraints in thestructured data of the newly updated knowledge base (updated by the newexperimental results), which can utilize a multitude of well-knownmethods for pattern recognition and machine learning, including withoutlimitation Bayesian regression and Bayes model comparison, probabilisticdiscriminative models, discriminant functions, neural networks, sparsekernel methods, Markov Random fields, K-means clustering, approximateinference, sampling (including Markov chain Monte Carlo, Gibbs samplingand hidden Markov models), kernel-Hilbert spaces, support vectormachines (SVMs), regression for string-to-string mapping, energy-basedmodels and linear dynamical systems (LDS) analysis, any and all of whichcan be programmed by a person having ordinary skill in the art withreference to and guidance from “Pattern Recognition and MachineLearning,” Christopher M. Bishop, Springer (2006), 738 pp., which ishereby incorporate by reference herein in its entirety.

Additional aspects of the reverse-engineering function in the DAE andModeling modules can be programmed by one having ordinary skill withguidance found in “Artificial Intelligence: Sixth Edition: Structure andStrategies for Complex problem Solving, George F. Luger, AddisonWesley/Pearson, (2008), 754 pp., and in “Paradigm of Artificialintelligence Case Studies in Common LISP”, Peter Norvig, (1992), MorganKaufmann, both hereby entirely incorporated by reference herein,including such methods and approaches as, without limitation, PROLOG,LISP, symbol-based machine learning, ID3 Decision tree Induction,unsupervised learning, version space search, perceptron learning,back-propagation learning (such as, for example, NETtalk), and naturallanguage programming (NLP).

Optimization of any step in the ARS modules, including for example,optimization in Experimental Chooser and optimizing fit of CongruenceModule with the user-specified goals and change in the knowledge base,can be programmed using any methods outlined by M. Athans and P. L Falbin “Optimal Control: An Introduction to the Theory and Applications,Dover Publications, Mineola, N.Y., (2007), 877 pp., which is herebyincorporated herein by reference in its entirety.

The DAE and Modeling modules can include, through distributed access,any number of analytical functions that can operate on data, wherein apreferred embodiment of the invention can include at least filtering,regression and correlation, a more preferred embodiment can additionallyinclude one or more of recursion analysis, hash tables, binary searchtrees and B-trees, and a most preferred embodiment can additionallyinclude methods for sub-linear association mining (SLAM), integratedBayesian Inference (IBIS), self-organizing maps (SOMs), andreverse-engineering, among other algorithms, wherein these module can beprogrammed accordingly by one having ordinary skill in the art and usingsuch techniques, methods and approaches as are provided in Brian D. O.Anderson, “Optimal Filtering, “Dover Publications (2005), Mineola, N.Y.,357 pp.; in “Mathematical Techniques for Biology and Medicine, WilliamSimon, (1987), Dover Publications, New York, N.Y., 295 pp.; in“Introduction to Algorithms, 2^(nd) Edition, Thomas H. Cormen et al.,MIT Press, Cambridge, Mass., (2001); “Statistical Digital SignalProcessing and Modeling”, Monson H. Hayes, John Wiley & Sons (1996), 608pp.; and in “Pattern Classification, 2^(nd) Edition”, Richard O. Duda,Peter E. Hart and David G. Stork, (2001), J. Wiley and Sons; all ofteachings are hereby incorporated herein by reference in their entirety.

Modeling Module (MM) The Modeling Module and/or Congruence Module can beused in developing and/or combining a domain knowledge base inconjunction with a domain-specific dynamic model (or simulation). TheARS can add and integrate through its modeling module one or more of aset of principles of general systems and principles of energeticsassociated with general systems models and/or the domain-specific modelsof the studied system. This can include, e.g., functions such as growthmodel functions, competition, structures, cooperation, decomposition,aggregation, decentralization, perturbation, stability, decentralizedcontrol, hierarchical models, subsystem analysis, and stability regions,among others, and these principles can be programmed readily by a personhaving ordinary skill in the art from methods described by Dragislov D.Siljak, in “Large-Scale Dynamic Systems: Stability and Structure,”(1978), Dover Publications, Mineola, N.Y., 416 pp., and from methodsdescribed in “Predicting Structured Data,” MIT Press, Cambridge, Mass.,edited by Gokhan Bakir et al., (2007), both of which are herebyincorporated by reference herein in their entirety.

Data Analysis Engine (DAE) and Congruence Module—Knowledge ModelAssembly

The Data Analysis Engine can include specific unique and customalgorithms and/or data analysis routines and/or it can provide aninterface (by ‘wrapping’ and/or interconnecting to) to multipleoff-the-shelf (OTS) commercial software packages that are well known tothose skilled in the art of data analysis, such as, for example withoutlimitation, Rosetta®, GeneSpring®, SAS®, Excel®, Spotfire®, GeneLinker®(Integrated Outcomes Software, Kingston, Ontario) and other packages).

Additional functionality can be programmed into the DAE according to oneembodiment, including evolutionary algorithms, fitness functions,multiple objective functions and constraint functions, cellular automataand neural systems, by one having ordinary skill in the art withguidance from “Bio-Inspired Artificial Intelligence: Theories, Methodsand Technologies,” Dario Floreano and Claudio Mattiussi, (2008), MITPress, Cambridge Mass. 659 pp., incorporate herein in its entirety byreference hereby.

Knowledge Base and Domain Ontology

The structure of the domain knowledge base that can be utilized by anARS according to an embodiment of the invention can be developed usingmethods that include, without limitation, KBs, backward and forwardchaining, rule formulation and search, object-oriented representation(objects and frames), structured descriptions, taxonomies, autoepistemiclogic, reasoning, vagueness principles, GOLOG, STRIPS and other aspectsof semantic knowledge representation such as can be programmed by aperson having ordinary skill in the art with the methods found in“Knowledge Representation and Reasoning,” Ronald J. Brachman and HectorJ. Levesque, Morgan Kaufman/Elsevier, New York, N.Y. (2004), 381 pp.,which is incorporated herein by reference in its entirety. Further, inimplementing code to direct the user interface and query manager toexamine correspondence between semantically related ontologies (such asthose of a prior knowledge base and of an updated knowledge base, orwhen simply searching for related ontologies in the domain, a programmerhaving skill in the art can be sufficiently guided by the methodsdescribed in “Ontology matching,” Jerome Euzenat and Paul Shvaiko,Springer, (2007), 334 pp., hereby entirely incorporated herein byreference.

Experiment Chooser and Congruence Module

The Experiment Chooser (ExpCh) and Congruence Modules (CM) according toembodiment of the invention can utilize multi-objective decisions,decision rules, scaling (including nominal, ordinal, interval, ratio andmulti-dimensional scaling), utility theory, vector optimization,weighting, assessment methodologies (including the ELECTRE method),priorities, goals and goal programming methods that can be readilyprogrammed by a person having ordinary skill in the art with referenceto “Multi-objective Decision Making Theory and Methodology,” VimChankong and Yacov Y. Haimes, Dover Publications, Mineola, N.Y., (1983),406 pp, the teachings of which are hereby incorporated herein byreference in their entirety.

Also relevant to the functionality of the Experiment Chooser, DAE andoptimizing the modeling steps according to an embodiment, one havingordinary skill in the art can program functional objects formulti-objective optimization, MO-evolutionary algorithm, multi-criteriadecision-making, fuzzy logic, Pareto ranking, goals, and utilityfunctions by referring to the methods contained in “EvolutionaryAlgorithms for Solving Multi-Objective Problems: 2^(nd) Edition,” CarlosA. Coello Coello, Gary Lamont and David Van Veldhuizen, Springer,(2007), 800 pp., hereby incorporated by reference herein in it entirety.

Computing System

Referring now to FIG. 26, there is illustrated a block diagram of acomputer operable to execute the disclosed architecture. In order toprovide additional context for various aspects of the subject invention,FIG. 26 and the following discussion are intended to provide a brief,general description of a suitable computing environment 2601 in whichthe various aspects of the invention can be implemented. While theinvention has been described above in the general context ofcomputer-executable instructions that may run on one or more computers,those skilled in the art will recognize that the invention also can beimplemented in combination with other program modules and/or as acombination of hardware and software.

Generally, program modules include routines, programs, components, datastructures, etc., that perform particular tasks or implement particularabstract data types. Moreover, those skilled in the art will appreciatethat the inventive methods can be practiced with other computer systemconfigurations, including single-processor or multiprocessor computersystems, minicomputers, mainframe computers, as well as personalcomputers, hand-held computing devices, microprocessor-based orprogrammable consumer electronics, and the like, each of which can beoperatively coupled to one or more associated devices.

The illustrated aspects of the invention may also be practiced indistributed computing environments where certain tasks are performed byremote processing devices that are linked through a communicationsnetwork. In a distributed computing environment, program modules can belocated in both local and remote memory storage devices.

A computer typically includes a variety of computer-readable media.Computer-readable media can be any available media that can be accessedby the computer and includes both volatile and nonvolatile media,removable and non-removable media. By way of example, and notlimitation, computer readable media can comprise computer storage mediaand communication media. Computer storage media includes both volatileand nonvolatile, removable and non-removable media implemented in anymethod or technology for storage of information such as computerreadable instructions, data structures, program modules or other data.Computer storage media includes, but is not limited to, RAM, ROM,EEPROM, flash memory or other memory technology, CD-ROM, digital videodisk (DVD) or other optical disk storage, magnetic cassettes, magnetictape, magnetic disk storage or other magnetic storage devices, or anyother medium which can be used to store the desired information andwhich can be accessed by the computer.

Communication media typically embodies computer-readable instructions,data structures, program modules or other data in a modulated datasignal such as a carrier wave or other transport mechanism, and includesany information delivery media. The term “modulated data signal” means asignal that has one or more of its characteristics set or changed insuch a manner as to encode information in the signal. By way of example,and not limitation, communication media includes wired media such as awired network or direct-wired connection, and wireless media such asacoustic, RF, infrared and other wireless media. Combinations of the anyof the above should also be included within the scope ofcomputer-readable media.

With reference again to FIG. 26, there is illustrated an exemplaryenvironment 2601 for implementing various aspects of the invention thatincludes a computer 2602, the computer 2602 including a processing unit2603, a system memory 2604 and a system bus 2605. The system bus 2605couples system components including, but not limited to, the systemmemory 2604 to the processing unit 2603. The processing unit 2603 can beany of various commercially available processors. Dual microprocessorsand other multi-processor architectures may also be employed as theprocessing unit 2603.

The system bus 2605 can be any of several types of bus structure thatmay further interconnect to a memory bus (with or without a memorycontroller), a peripheral bus, and a local bus using any of a variety ofcommercially available bus architectures. The system memory 2604includes read only memory (ROM) 2606 and random access memory (RAM)2607. A basic input/output system (BIOS) is stored in a non-volatilememory 2606 such as ROM, EPROM, EEPROM, which BIOS contains the basicroutines that help to transfer information between elements within thecomputer 2602, such as during start-up. The RAM 2607 can also include ahigh-speed RAM such as static RAM for caching data.

The computer 2602 further includes an internal hard disk drive (HDD)2608 (e.g., EIDE, SATA), which internal hard disk drive 2608 may also beconfigured for external use in a suitable chassis (not shown), amagnetic floppy disk drive (FDD) 2609, (e.g., to read from or write to aremovable diskette 2610) and an optical disk drive 2611, (e.g., readinga CD-ROM disk 2612 or, to read from or write to other high capacityoptical media such as the DVD). The hard disk drive 2608, magnetic diskdrive 2609 and optical disk drive 2611 can be connected to the systembus 2605 by a hard disk drive interface 2613, a magnetic disk driveinterface 2614 and an optical drive interface 2615, respectively. Theinterface 2613 for external drive implementations includes at least oneor both of Universal Serial Bus (USB) and IEEE 1394 interfacetechnologies.

The drives and their associated computer-readable media providenonvolatile storage of data, data structures, computer-executableinstructions, and so forth. For the computer 2602, the drives and mediaaccommodate the storage of any data in a suitable digital format.Although the description of computer-readable media above refers to aHDD, a removable magnetic diskette, and a removable optical media suchas a CD or DVD, it should be appreciated by those skilled in the artthat other types of media which are readable by a computer, such as zipdrives, magnetic cassettes, flash memory cards, cartridges, and thelike, may also be used in the exemplary operating environment, andfurther, that any such media may contain computer-executableinstructions for performing the methods of the invention.

A number of program modules can be stored in the drives and RAM 2607,including an operating system 2616, one or more application programs2617, other program modules 2618 and program data 2619. All or portionsof the operating system, applications, modules, and/or data can also becached in the RAM 2607. It is appreciated that the invention can beimplemented with various commercially available operating systems orcombinations of operating systems.

A user can enter commands and information into the computer 2602 throughone or more wired/wireless input devices, e.g., a keyboard 2620 and apointing device, such as a mouse 2621. Other input devices (not shown)may include a microphone, an IR remote control, a joystick, a game pad,a stylus pen, touch screen, or the like. These and other input devicesare often connected to the processing unit 2603 through an input deviceinterface 2622 that is coupled to the system bus 2605, but can beconnected by other interfaces, such as a parallel port, an IEEE 1394serial port, a game port, a USB port, an IR interface, etc.

A monitor 2623 or other type of display device is also connected to thesystem bus 2605 via an interface, such as a video adapter 2624. Inaddition to the monitor 2623, a computer typically includes otherperipheral output devices (not shown), such as speakers, printers, etc.

The computer 2602 may operate in a networked environment using logicalconnections via wired and/or wireless communications to one or moreremote computers, such as a remote computer(s) 2625. The remotecomputer(s) 2625 can be a workstation, a server computer, a router, apersonal computer, portable computer, microprocessor-based entertainmentappliance, a peer device or other common network node, and typicallyincludes many or all of the elements described relative to the computer2602, although, for purposes of brevity, only a memory storage device2626 is illustrated. The logical connections depicted includewired/wireless connectivity to a local area network (LAN) 2627 and/orlarger networks, e.g., a wide area network (WAN) 2628. Such LAN and WANnetworking environments are commonplace in offices, and companies, andfacilitate enterprise-wide computer networks, such as intranets, all ofwhich may connect to a global communication network, e.g., the Internet.

When used in a LAN networking environment, the computer 2602 isconnected to the local network 2627 through a wired and/or wirelesscommunication network interface or adapter 2629. The adaptor 2629 mayfacilitate wired or wireless communication to the LAN 2627, which mayalso include a wireless access point disposed thereon for communicatingwith the wireless adaptor 2629.

When used in a WAN networking environment, the computer 2602 can includea modem 2630, or is connected to a communications server on the WAN2628, or has other means for establishing communications over the WAN2628, such as by way of the Internet. The modem 2630, which can beinternal or external and a wired or wireless device, is connected to thesystem bus 2605 via the serial port interface 2622. In a networkedenvironment, program modules depicted relative to the computer 2602, orportions thereof, can be stored in the remote memory/storage device2626. It will be appreciated that the network connections shown areexemplary and other means of establishing a communications link betweenthe computers can be used.

The computer 2602 is operable to communicate with any wireless devicesor entities operatively disposed in wireless communication, e.g., aprinter, scanner, desktop and/or portable computer, portable dataassistant, communications satellite, any piece of equipment or locationassociated with a wirelessly detectable tag (e.g., a kiosk, news stand,restroom), and telephone. This includes at least Wi-Fi and Bluetooth™wireless technologies. Thus, the communication can be a predefinedstructure as with a conventional network or simply an ad hoccommunication between at least two devices.

Advantages and Importance

The method and system according to preferred embodiments providing forautomated biomedical research systems provide for more rapid andcustomized access to state-of-art computational analyses using aneasy-to-use user interface, where researchers can access experimentaltechniques, results and data analyses without needing the specificexpertise in-house (i.e., the expertise is made accessible via the ARSfrom numerous experts who build the intelligent Experiment Objects thatare accessed by the system.

The innovation disclosed herein brings to the automated researchindustry at least the following:

-   -   Firstly, the invention facilitates standard equipment control        methodologies and terminologies for automation software        solutions using the S88 modularization practices and methods        within the equipment, between like pieces of equipment, and        across the research environment from Facility Applications (FA)        to Process Tool and back end test, assembly, and related        applications.    -   Secondly, the invention reduces costs, time to market, and        increases reliability by utilizing commercial S88-based software        packages to modularize the software code for greater engineering        efficiency and quality.    -   Thirdly, improved equipment utilization and experimental result        throughput is achieved by implementing the smart rules engine in        conjunction with the S88-based control code in order to optimize        the flow of experimental result through an individual piece of        equipment or group(s) of equipment. The rules engine utilizes        real-time process information and responds to system prompts to        make intelligent decisions based on rule sets designed by a        process expert and implemented by a control system expert.    -   Finally, an optimized research process is achieved by providing        real-time streaming research data to achieve run-to-run        comparisons, apply statistical process control, apply Adaptive        Control Methodologies, enable e-bioresearch equipment evaluation        with Security, and enable Genealogy for each Biotechnology and        biomedical experimental result and/or individual component of        new biomedical knowledge.

This innovation increases reliability by creating, testing, andimplementing S88 control and equipment modules in the controller; thecreation of EO recipes from this point forward includes linking togetherpre-tested equipment module logic, thus increasing the overallreliability of the execution layer.

One or more further aspects of the invention can be illustrated by oneor more preferred embodiments which provide for a computer-based andInternet-based method of facilitating a transaction online between acustomer and a third-party service provider, wherein the online methodcan be performed by a single facilitator company and wherein the methodcomprises the steps of providing an online, computer-based transactionsystem communicably coupled to a database, storing service-orderinformation relating to a plurality of services and service providers inthe database, wherein at least service-providers can access the databasevia a first interface and enter information into the database and atleast one customer can access the website and/or database via a secondinterface, displaying on a web site offered services from the database,which step of displaying services can include providing an onlinepresentation or menu of at least one of medical, biomedical, medicalresearch and biomedical research services selectable by a customer, saidservices performable by at least one medical, biomedical, medicalresearch and biomedical research service provider, providing an offeronline to sell at least one service, detecting an order on a computerfrom a customer for the at least one service, generating a service orderautomatically by a computing module, and outputting the automaticallygenerated service-order from a computer.

A further aspect of the invention can be illustrated by an e-commerceweb site that offers at least one medical and/or biomedical service,such as, for example, providing for a parent and/or guardian of asoon-to-be born child to order, schedule and/or purchase the service ofsampling, preserving and storing their child's stem cells in a mannerthat allows the parent easy long-term storage and control over thesample.

A further preferred embodiment of the invention provides a system forgenerating a stem-cell-storage service order, relative to a prospectivenew-born baby or an adult customer, or both, comprising: a databasestoring stem-cell-storage service-order information about each of aplurality of medical device service providers; and a computer systemcommunicably coupled to the database, the computer system includingcomputer program instructions to:

(a) provide a first user interface to accept, from a first user, anidentification of one of the plurality of medical service providers andinformation identifying a service provider who is to be authorized toprovide the service of stem-cell extraction, preservation, packaging andpackage delivery, relative to a baby, a customer, or both;

(b) determine if the first user is authorized to enter information intothe database;

(c) if the first user is authorized, store information provided by thefirst user in the database in association with the one of the pluralityof medical service providers;

(d) provide a second user interface to accept user credentials and anidentification of one of the plurality of medical service providers,wherein the second user interface is accessible by a customer who is notthe first user or one of the plurality of medical service providers;

(e) determine if the customer is authorized to generate astem-cell-storage service-order, relative to the one of the plurality ofmedical service providers, comprising accessing the database todetermine if the user credentials accepted from the customer by thesecond user interface correspond to information requirements criteriastored in the database in association with the one of the plurality ofmedical service providers; and

(f) if the customer is authorized, generate a service order relating tothe one of the plurality of medical service providers and provide thegenerated service order to the medical service provider.

Ordering and Purchasing Step

Upon signing up for obstetrician's care, or signing up for the hospitalbirthing services, or by one or more other means at a time prior and/orproximate to the child's birth, a requesting parent can order andpurchase the taking of sample, and or further pre-pay for thepreservation and long-term storage of the stored stem cells. Forinstance, FIG. 19 illustrates a sign-up or purchase/order form that canallow a parent and guardian of a soon-to-be born child and/or a newbornchild to order or schedule the storage service to be conducted and tospecify the delivery of the stored product to the purchasing parent andor guardian and/or to the parent or guardian's representative.

The sign-up or purchase form can be on paper, or can be electronic viathe Internet and a web service. It is considered within the scope of theinvention to provide a web-page presented service for such cord-bloodstem cell storage, whereby the service provided arranges directly withthe attending physician and/or the birthing clinic or the hospital toprovide the desired storage procedure according to the service detailsselected.

FIGS. 4, 19, 21 and 31 illustrate a service presentation and customersign-up procedure effected over the Internet, with the data being movedto a server and then a secondary order being placed by the service,based on the information in the form, with a forwarded order sheet beingdelivered by the Internet to participating medical providers, doctors,technicians, clinics and or hospitals. According to known methods, suchas, for example, by use of RSA public-key encryption, this orderingservice can utilize encryption to keep information confidential.

It is within the scope of the invention to provide further for automatedforwarding and/or automated secondary ordering procedures, whereby theparent and or guardian requestor (or a representative of said party) canprepay for the service on a web-page, and the automated service-order isforwarded to the medical professionals and service professionalsresponsible for attending the birth, and whereby the stored and sealedsample is collected, extracted, preserved, contained, sealed, packagedand posted directly from the birthing clinic and/or hospital, or by theprimary attending medical professionals, and delivered to the storagefacility or to the customer for long-term storage. Alternatively, anintermediary service step can occur whereby an intermediate teamreceives cryo-preserved blood samples from the hospital and/or medicalclinic and/or birthing location and this team performs the preservationand storage-sealing steps and delivers the completed stored samples tothe requesting parent and/or guardian, and/or to the representative(s)of same.

Example 19

A preferred embodiment of the business method according to the inventionenables a parent of a soon-to-be child to browse to a web-site thatoffers the service according to the invention. The requesting parentorders the taking of sample, and pre-pays for the preservation andlong-term storage of the stem cells. In alternative embodiments of theinvention the processing materials can include an address label so thatthe technician's mailing of a completed stored sample can be madedirectly to the requesting parent. Additionally, the delivery can be bycourier service or mailing service that provides careful tracking of thedelivery for greater security.

Example 20

Embodiments of the invention can be further illustrated with referenceto the Figures. Referring to FIG. 27, a container 25, which can be, forexample, a plastic, glass or metal ampule or vial, contains preservedstem cells 24, immediately surrounded by a specialized environment 12,which environment can be, without limitation, a vacuum, a nitrogen gas,a gas mixture low in oxygen or devoid of oxygen, or a denser medium,such as a non-reactive plastic, or a preservative gel or otherpreserving medium. An ampule with a relative vacuum or low oxygenenvironment can be created as in FIG. 28A, wherein a glass flame-offpipette 22 containing stem cells 24, is located adjacent a stopcock 22in a glass vacuum rack, with the glass pipette 22 being sealablyconnected by a greased, ground-glass stopper/flange 26. A vacuum 28 canbe exerted (by a vacuum pump or cryotrap) through a conduit in thestopcock 22 to pull air and/or oxygen out of the pipette 22. Referringto FIG. 28B, the flame-off step is accomplished while a vacuum is beingexerted (or a non-oxygen environment maintained), with the pipette heldby tongs 29 as a torch flame 27 heats the pipette above the stored stemcells and then the melted portion of the pipette is stretched in adownward direction 23 to pull the melting glass closed at a narrow waistregion. At this narrow waist point, after melting the tube closed, theglass can rapidly cool after removal of the flame 27 and the glass waisteasily snapped to form ampule 25. A two-layer storage containeraccording to an embodiment is illustrated in FIG. 29, wherein anairtight, permanently sealed ampule 25 containing stem cells 24 in thepresence of a vacuum or nitrogen environment (or inert gas, orprotective medium) 12 is located with an outer storage cylinder 32having a screw cap 34, and with cotton wadding 36 further protecting theampule 25.

FIG. 21 illustrates a succession of web pages or web screens that canappear according to an embodiment as part of the business method ofproviding an offer to a potential customer and the recording of orderinformation and completion of the ordering transaction, wherein: a firstbusiness offering web page 2101 can present to the customer a choice ofobtaining a description of services and/or a hyperlink to begin anorder; a subsequent web page 2102 can include data-entry (or text-entry)windows 2104, which can include pull-down data selection windows (ormenus of participating professionals, hospitals and clinics), for thecustomer to enter identifying and transaction information, such as butnot limited to customer name and address, physician, hospital or clinicand/or expected birth date (or estimated sampling procedure date); afurther secondary screen 2103 containing level of service choices 2106(e.g., an inexpensive customer option can include simplestsampling/extraction/storage method, information storage and simpleampule sent to customer, whereas a more expensive option may include anexpensive ampule, expensive sampling method, more elaborate and/orcomplete storage information and record, dual storage in safe-depositbox with a partner bank and in an ampule sent to the customer) a furthersecondary screen 2105 providing cost, invoice and/or payment information(for example, without limitation, an initial order fee, a specimenpreparation fee, a storage container fee, shipping fees and, if the SSPCompany is providing custodial storage, an annual storage fee); afurther screen 2107 providing legal terms, which screen can include aninteractive button to register customer's agreement to the legal terms(such as, without limitation, providing notice to the child at age ofmajority); and, inter alia, a further page 2108 that can include aninteractive button to cause the order to be generated, and/or toinitiate the processing of the submitted order.

FIG. 19 illustrates business steps according to another embodiment,wherein: at step 1901 a customer places an order; at step 1902 the orderinformation is received and/or registered and/or recorded; at step 1903the order information is matched against a database listing 1904; atstep 1905 a service order is generated, which can be an automated step;at step 1906 a service order memo is created in email and or printed (inwhich fields are automatically filled in from the order informationand/or the database information extracted in correspondence to the orderinformation. The memo can read as follows, or in similar fashion:

“Dear <<SERVICE PROVIDER>> Please carry out standard procedure <<STORAGEI>> for customer <<XYZ>>. Attached are procedure protocols, specimenmailers and payment details. Sincerely, <<SSP Company>>”

At step 1907 the medical procedure is specified from a rule-basedsoftware engine that links the customer information and desired locationfor the procedure to appropriate and available protocols that are storedand indexed in the SSP Company database (which can include specificprotocols for the blood and/or tissue sampling, stem cell extraction,cell freeze-drying, preservation and ampule storage), a mailerspecification and label are generated, and payment to the serviceprovider is detailed and scheduled; and step 1908 is transmission of thecompleted service order to the medical professional. Steps 1906, 1907and 1908 can be automated.

FIG. 30 illustrates a storage ampule 25 containing stem cells 24 havinga covering layer 62 upon which is permanently affixed a data component64, which data component 64 can be, for example, without limitation, barcode, optical storage, microfiche or other information record.

Example 21

A further embodiment provides another example, illustrated by referenceto FIG. 31, of a sequence of business steps according

to the invention. FIG. 31 generally shows a flow chart describing aWeb-based ordering process that is connected to automated generation ofservice-order that direct the steps of performing medical service steps,packaging and delivering storage containers to a storage site or to acustomer; and/or delivering to the customer physical or digital keys toaccess the storage site, and/or code-release keys to initiate physicaldelivery of the stored vial to the customer. In more detail, thefollowing steps are illustrated in FIG. 31. At step 720, a customeraccesses a web site that offers the storage service or storablestem-cell product. At step 730, the customer orders services on the website, such as, for example, providing medical professional andhospital/clinic information, providing dates, choosing level of service,entering data or meta-data to be included later in (or on) the storagecontainer. This information can include names, dates, parents' DNA data,parents' medical history, inter alia, as well as information that may besubsequently and automatically pulled from other 3rd party databasesthrough the Internet in response to information entered by the customer.At step 740, the customer pre-pays for extraction and storage (such as,for example, paying by credit card, or paying by pass-through billing toan obstetrician and/or through hospital and/or clinic fees). At step750, the customer agrees to a binding contract (including, withoutlimitation, a legal electronic signature, a waiver concerning liability,and/or the customer expressly assuming risk and liability on behalf ofthe minor child). At step 760, the SSP company server connects thecustomer's order information to a SSP company database or to one or more3^(rd)-party databases to obtain additional data or information to beused in generating a service order and/or used in subsequent formationand delivery of the storage method and service, such as, withoutlimitation, information about procedures, locations, practitioners,hospitals, clinics, regulations, materials, storage facilities, banks,costs, risks, re-infusion, probabilities, service delivery, postaldelivery, scientific data, storage containers and other informationrelated to the customer-provided information and/or related toinformation needed for the service order. Typically, this data will bepulled from the SSP company and 3rd party databases by software programroutines that automatically generate the service order; some of thisinformation can be independent of the information provided by thecustomer's order entry, while other information can be dependent uponthe customer's order entries. At step 770, the company softwareautomatically generates a service order to participating professionals,hospitals, clinics and/or other service providers. At step 780, the SSPcompany software transmits electronically the Service Orders and portionof prepayment to practitioners, hospitals and clinics (optionallyincluding a preaddressed postal or courier mailer envelope that can besubsequently used by the medical professional(s) to send the sampled andstored stem cells directly to a chosen storage facility or to thecustomer). At step 790, the medical and preservation services areperformed by the professional practitioners in the hospital or clinic(for example, without limitation the umbilical cord is sectioned, bloodextracted, stem cells extracted, stem cells preserved, stem cells storedin a storage container, and the container labeled with data and/or codetracking information). At step 800, data is entered by medicalprofessionals, converted and/or transferred automatically onto the datacomponent that is to be stored with the stored stem cells (such as, forexample, information about the child/patient, the birth event, thesampling, extraction and preservation procedures. At step 810 the datacomponent is merged with the storage container, such as by an automatedlabeling procedure. At step 820, the storage container is packaged in amailer, which can be a mailer that has been preaddressed to the customeror to a centralized storage facility (such as described at step 780above). At step 830, the mailer is sent/delivered to the customer, suchas by U.S. postal service or by a courier service, and/or, at step 840,the package can be sent to a centralized storage facility or to a3^(rd)-party storage location, such as a safety-deposit box in a bank.At step 850, a code or key can be sent to the customer allowing laterrecovery from the storage facility or safety deposit box (where the codecan be a digital password that allows the customer to signal a storagefacility to automatically ship the stored sample to the customer or toanother 3rd party).

FIG. 32 shows a transmittal envelope 90 that is especially suited and/ordesigned for a particular storage container 98 and for routing byaddressing 94 to a particular storage facility, whereby the transmittalenvelopes bear prepaid postage % and sufficient identifying coding 92 sothat the envelopes can be automatically manipulated by robotic handling,including being processed, stored and later retrieved and reshipped toan end-user customer based on the information in the exterior coding 92.In the storage facility, automated storage methods can include providingsorting, processing, aligning and storage placement machinery whereinthe specialized envelopes are stored efficiently and compactly instorage carousels and/or other automated containers that allow machineaccess, deposit and retrieval and automated reshipment based on thestorage facility receiving a password-protected, storage-releasedirective from an end-user customer.

What has been described above includes examples of the invention. It is,of course, not possible to describe every conceivable combination ofcomponents or methodologies for purposes of describing the subjectinvention, but one of ordinary skill in the art may recognize that manyfurther combinations and permutations of the invention are possible.Accordingly, the invention is intended to embrace all such alterations,modifications and variations that fall within the spirit and scope ofthe appended claims. Furthermore, to the extent that the term “includes”is used in either the detailed description or the claims, such term isintended to be inclusive in a manner similar to the term “comprising” as“comprising” is interpreted when employed as a transitional word in aclaim.

While the present invention has been described in conjunction with oneor more preferred embodiments, one of ordinary skill, after reading theforegoing specification, will be able to effect various changes,substitutions of equivalents, and other alterations to the systems andmethods set forth herein. It is therefore intended that the patentprotection granted hereon be limited only by the appended claims andequivalents thereof.

What is claimed is:
 1. A web-based and computer-implemented systemcomprising: one or more processors residing on an application server, atleast one processor electronically connected to a memory storingnon-generic, specialized machine-readable instructions; a databaseelectronically connected to at least one processor, the databaseconfigured to store information about a plurality of medical orbiomedical service providers that offer medical or biomedical services,wherein the offered biomedical services are related to a plurality offunctional biomedical objectives, and wherein each functional biomedicalobjective is related in the database to one or more medical orbiomedical service providers; wherein one or more of the non-generic,specialized instructions instruct the one or more processor to performthe following: host one or more web pages displaying information aboutone or more medical or biomedical services; allow a customer to inputinformation to the system relative to the customer; select a medical orbiomedical service that is related to a functional objective for acustomer accessing the system, and wherein the system is configured suchthat a customer accessing the application server through an Internetconnection causes the system software to perform the steps of:determining a user-specified goal based on user input about choice of astudied-system domain, the studied system having components and dynamicrelationships between components, and further user settings from thegroup consisting of goal type and sub-goal type, budget, deadline andrelevant domain database source, assembling, via aknowledge-base-assembly software module responsive to a user's inputchoice of studied-system, a knowledge-base-assembly comprising agraph-based, causal-network computer model related to at least asub-system of the studied system, enabling and constraining forwardsimulation of the graph-based, causal-network computer model, by adevice, to reveal effects from upstream perturbations on downstreambehavior in the studied system by using variables from the groupcomprising time-dynamic data, energy parameters, material balances andthermodynamic variables, said thermodynamic variables being from thegroup comprising temperature, Gibbs free energy, enthalpy, entropy andelectrical potential, constructing automatically, by a device, aresearch goal statement based on the user-specified goal selection, thestatement having parameter settings that enable gathering expertinformation from a studied-system domain manual related to thecomponents and relationships between components in the studied system,transforming, by a device, the research goal statement into adeclarative statement of information that is needed to reduceuncertainty in the knowledge assembly by evaluating a studied-systemknowledge model in view of the research goal statement and by analyzingthe highest probability path to reduce a gap in information thatcorresponds with uncertainty in the knowledge assembly; selecting,automatically by a computer device, one or more research services, basedon the services providing procedures or protocols likely to produceexperimental results to reduce uncertainty in the knowledge assembly,wherein the software-directed steps relate the customer's inputinformation to information about the biomedical services and retrievefrom the database information sufficient to complete and deliver astandard service order.
 2. The system of claim 1, wherein at least oneof said functional biomedical objectives comprises at least one of thegroup of (a) characterizing normal system function, (b) one or more ofdetecting and characterizing abnormal system function and detecting andcharacterizing system dysfunction, (c) one or more of testing andfinding modes for correcting system function and testing and findingadaptive and protective modes for system function, and (d) optimizingmodes for correcting, adapting or protecting system function.
 3. Thesystem of claim 1, wherein at least one of the functional biomedicalobjectives comprises a biomedical service product comprising theperformance of a biomedical service protocol by a third-party vendorsubsequent to and in response to the service order.
 4. The system ofclaim 3, wherein the biomedical service product is a biomedical researchservice product comprising the performance of a biomedical researchservice protocol by a third-party vendor subsequent to and in responseto the service order.
 5. A computer-based and internet-based biomedicalservice business method comprising the steps of: (a) providing one ormore processors residing on an application server, at least oneprocessor electronically connected to a database and accessible tocustomers via an Internet connection; (b) storing information in thedatabase about a plurality of biomedical service providers offeringbiomedical services, the offered biomedical services being related to aplurality of functional biomedical objectives, and wherein eachfunctional biomedical objective is related in the database to one ormore biomedical service providers; (c) communicating, by a computerdevice, information about the service providers to potential customers;(d) receiving, by a computing device, from a customer, input informationto the application server relative to the customer; (e) initiating, by acomputer device, a query based on input information from the customer;comprising the performance of a biomedical service protocol by athird-party vendor subsequent to and In response to the service order;(f) receiving, by a computer device, information from one or morecustomers with respect to a customer's choosing of a service of aservice provider; (g) relating, by a computer device, the customer'sinput information to information about the biomedical services by thefurther steps of: (g.1) determining a user-specified goal based on userinput about choice of a studied-system domain, the studied system havingcomponents and dynamic relationships between components, and furtheruser settings from the group consisting of goal type and sub-goal type,budget, deadline and relevant domain database source, (g.2) assembling,via a knowledge-base-assembly software module responsive to a user'sinput choice of studied-system, a knowledge-base-assembly comprising agraph-based, causal-network computer model related to at least asub-system of the studied system, (g.3) enabling and constrainingforward simulation of the graph-based, causal-network computer model, bya device, to reveal effects from upstream perturbations on downstreambehavior in the studied system by using variables from the groupcomprising time-dynamic data, energy parameters, material balances andthermodynamic variables, said thermodynamic variables being from thegroup comprising temperature, Gibbs free energy, enthalpy, entropy andelectrical potential, (g.4) constructing automatically, by a device, aresearch goal statement based on the user-specified goal selection, thestatement having parameter settings that enable gathering expertinformation from a studied-system domain manual related to thecomponents and relationships between components in the studied system,(g.5) transforming, by a device, the research goal statement into adeclarative statement of information that is needed to reduceuncertainty in the knowledge assembly by evaluating a studied-systemknowledge model in view of the research goal statement and by analyzingthe highest probability path to reduce a gap in information thatcorresponds with uncertainty in the knowledge assembly; and (g.6)selecting, automatically by a computer device, one or more researchservices, based on the services providing procedures or protocols likelyto produce experimental results to reduce uncertainty in the knowledgeassembly; (h) retrieving from the database, by a computer device,information sufficient to complete and deliver a standard service order;(i) completing, by a computer device, the service order; and (j)outputting, by a computer device, the service order and delivering, by acomputer device, the service order.
 6. The method of claim 5, whereinsaid functional biomedical objectives comprise at least one of the groupof (a) characterizing normal system function, (b) one or more ofdetecting and characterizing abnormal system function and detecting andcharacterizing system dysfunction, (c) one or more of testing andfinding modes for correcting system function and testing and findingadaptive and protective modes for system function, and (d) optimizingmodes for correcting, adapting or protecting system function.
 7. Themethod of claim 5, wherein at least one of the functional biomedicalobjectives comprises a biomedical service product comprising theperformance of a biomedical service protocol by a third-party vendorsubsequent to and in response to the service order.
 8. The method ofclaim 7, wherein the biomedical service product is a biomedical researchservice product comprising the performance of a biomedical researchservice protocol by a third-party vendor subsequent to and in responseto the service order.
 9. The method of claim 5, wherein the steps of(h)-(j) further comprise the steps of detecting an order on at least oneof a computer and the application server from the customer for at leastone of the functional biomedical objectives, connecting or relating viathe database the customer's order to information about at least onebiomedical service provider offering a biomedical service capable ofaccomplishing the at least one of the functional biomedical objectives,retrieving, by a computing device, from the database informationsufficient to complete a service order to the at least one biomedicalservice provider who is offering a biomedical service capable ofaccomplishing the at least one of the functional biomedical objectivescorresponding to the customer's order, and notifying the at least onebiomedical service provider of the customer's order by completing aservice order, by a computer device, and delivering the service order,by a computing device, to the at least one biomedical service provider.